VWHPIRUVWURNHUHKDELOLWDWLRQ - Forside - …vbn.aau.dk/files/52710516/MRCP_based_brain_computer... · 2011-06-01 · Title: MRCP-based brain-computer interface system for stroke rehabilitation

Marko Niemeier

8

Department of Health Science and Technology

Aalborg University

Fredrik Bajers Vej 7Phone +45 99 40 99 40Fax + 45 98 15 40 08http://www.hst.aau.dk

Title: MRCP-based brain-computer interfacesystem for stroke rehabilitation

Subject: Analysis and modelling and/ordesign and implementation of medical physi-ological or technological systems

Project period: P8, spring semester 2011

Project group: 855a

Participants:

Marko Niemeier

Anders Schierup

Duy Thien Van

Xintong Zhang

Supervisors:

Imran Khan NiaziNatalie Mrachacz-Kersting

Copies: 7

Pages: 100

Finished: 1 June 2011The reports content is freely accessible,but publication (with references) may be madeonly after agreement with the authors.

Abstract:

Stroke is the second most frequent type ofcardiovascular diseases in the world withabout 15 million incidents yearly. The costsassociated with stroke rehabilitation arevery high even though none of the currenttherapies provides significant improvementsfor recovery of motor dysfunctions.The introduction of brain-computer interface(BCI) might be a new way to induce neuralplasticity, because a combination of variousfeedback can be provided to the patient in anactive manner.

This project aims on the development of a BCIsystem based on movement-related corticalpotential (MRCP) used for stroke rehabilita-tion. The system is capable to detect the initialnegative phase of MRCPs online, which canbe observed in stroke patients who are tryingto move their impaired limb. The idea is totrigger the intended movement subsequentlywith an electrical stimulation and therebyinduce neural plasticity. Another focus of thereport is the development of a graphical userinterface, which meets the requirements toestablish the purposed rehabilitation method.

The final system was evaluated by a usabil-ity test and a performance test on healthy sub-jects. The usability test, carried out on foursubjects with prior knowledge about the basicprinciples of the system, showed besides mi-nor corrections a good usability. From the per-formance test of motor imagery tasks an aver-age true positive rate of 66.23 % for detectedmovement intentions could be obtained. Theresults were only based on two subjects andtherefore no statistical evidence can be con-cluded. However the test results indicate thatthe system is working and can be used for ex-perimental purposes.

PrefaceThis report has been produced by project group 855a on the 2th semester of the master educationin Biomedical Engineering and Informatics at Aalborg University.The theme for the semester is ”Analysis and modelling and/or design and implementation of med-ical physiological or technological systems”.The report applies to fellow students and others interested in the area.

Aalborg University, June 2011.

Reading instructions

The report is devided into four main parts:

• Part I: Problem analysis

• Part II: Problem solving

• Part III: Synthesis

• Part IV: Appendices

References are indicated by the Harvard method, where the author’s surname and the book’s pub-lication year is listed as the following example: [Surname, year]. In the report references are bothindicated before and after a full stop. If a reference is indicated before a full stop it refers only tothe sentence, whereas if it stands after a full stop it refers to the entire section.

I

Contents1 Introduction 1

I Problem analysis 3

2 Neurophysiological background 52.1 Movement control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Voluntary movement control . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3 Movement impairment in stroke patients . . . . . . . . . . . . . . . . . . . . . . 9

3 Stroke rehabilitation approaches 113.1 Physiotherapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.2 Constraint-induced movement therapy . . . . . . . . . . . . . . . . . . . . . . . 113.3 Robotic therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.4 Functional electrical stimulation . . . . . . . . . . . . . . . . . . . . . . . . . . 123.5 Paired associative stimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.6 Motor imagery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 Rehabilitation with brain-computer interface systems 174.1 Brain-computer interface system . . . . . . . . . . . . . . . . . . . . . . . . . . 174.2 Inducing neural plasticity by using brain-computer interface . . . . . . . . . . . 174.3 Brain-computer interface based on movement-related cortical potentials . . . . . 18

5 Problem formulation 21

II Problem solving 23

6 Requirements 256.1 System description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256.2 General requirements specifications . . . . . . . . . . . . . . . . . . . . . . . . 266.3 Individual requirements specifications . . . . . . . . . . . . . . . . . . . . . . . 26

7 Design 297.1 Design of the graphical user interface . . . . . . . . . . . . . . . . . . . . . . . 29

8 Implementation 358.1 Implementation of the graphical user interface . . . . . . . . . . . . . . . . . . . 35

9 Testing 439.1 Tests of the developing system . . . . . . . . . . . . . . . . . . . . . . . . . . . 439.2 Test of the finished system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519.3 Test summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

III Synthesis 55

10 Discussion 57

III

CONTENTS

11 Conclusion 59

IV Appendices 69

A Hebbian principle 71

B Transcranial magnetic stimulation 73

C Electroencephalography 75

D Movement-related cortical potentials 79

E Experimental protocol for performance testing 83

F Usability test 87

IV CONTENTS

Chapter 1

IntroductionThe World Health Organization (WHO) estimates that approximately 15 million people yearlyworldwide suffer a stroke, and of these 5 million die and another 5 million are left permanentlydisabled. Stroke is the second most frequent type of cardiovascular diseases and as a result, strokeis one of the main single causes of severe disabilities in the developed world. [Mackay and Men-sah, 2004]According to the Danish National Patient Registry (DNPR), Denmark had about 12,500 cases ofhospitalization from stroke in 2009 and about 9,500 cases of hospitalization from traumatic braininjury and other forms of acquired brain injury. Based on statements in the DNPR and an expertassessment, it is estimated that 4,500 to 8,000 persons yearly acquire brain injury and are in needof rehabilitation. [Sundhedsstyrelsen, 2011]The cost associated with rehabilitation is very high in Denmark. A person who was brain damagedin 2004 costed a total of 181,000 DKK the first year and 300,000 DKK in total over the next fiveyears. The cost for the first year was distributed on hospital costs equivalent to approximately 42% and a loss of productivity and municipal costs of roughly equal size. In 2008, when the healthcare costs include several more parameters, the overall costs were 110,000 DKK, including theincident year and the following year. The total costs of brain injury treatment and rehabilitationover the first two years, calculated from the 2008-numbers, were 270,000 DKK. None of thesenumbers cover all costs, but indicates that there are high and persistent high costs of brain injuryand subsequent rehabilitation. [Sundhedsstyrelsen, 2011]

Stroke is caused by disturbance of the blood supply to the brain and is classified in two categories;ischemic stroke and hemorrhagic stroke. Ischemic stroke is the most common type of stroke and iscaused by decreasing blood supply to part of the brain due to circulatory blockage. Hemorrhagicstroke is caused by leakage of blood caused by a ruptured blood vessel or an abnormal vascularstructure. Strokes lasting only seconds can cause neurological symptoms, while strokes lastingminutes can cause irreversible neuronal damage. If the primary motor cortex (M1) is affected bya disruption of blood supply the consequences are varying degrees of paralysis of the representedmuscles. The consequence of a stroke can be fatal for the patient. After surviving a stroke theperson may still suffer from a series of disabilities, most likely problems related to motor control.Stroke also affects the quality of life and changes daily life drastically at the workplace, at homeand in the community. [Guyton and Hall, 2006]

Treatment for stroke survivors can be divided in the two stages, acute stroke treatment and poststroke rehabilitation. The aim of acute stroke treatment is to limit the neurological damage of thestroke and to ensure the survival of the patient. Post stroke rehabilitation address the restoration orimprovement of lost body functions, which will provide a better quality of life and help the patientto return back to a normal life. [Guyton and Hall, 2006]Different rehabilitation strategies have been attempted for recovery of motor dysfunctions, butnone of the current therapies provides significant improvements and thus remain a therapeuticproblem [Birbaumer et al., 2008].

Physiotherapy is a classical stroke rehabilitation approach which aims on providing the patient aphysical basis by performing certain movements under the supervision of physiotherapist [Guytonand Hall, 2006]. A more modern method is rehabilitation with robotic devices, which have beenwidely used over the past 15 years [O’Dell et al., 2009]. The method is used to assist exercise and

1

quantify movements in stroke rehabilitation, however it takes a long time to get familiar with therobot and the training path is restricted [Fasoli et al., 2004]. Another technique of rehabilitation isfunctional electrical stimulation (FES) of muscles. FES provides short coordinated bursts of elec-tricity to weak muscles to facilitate functional movement. The design of the stimulation circuitand electrode is a crucial factor in the application of FES in order to acquire correct conductionfrom the stimulation current to the target tissue. [Rushton, 1997]Stroke rehabilitation through motor imagery (MI) is a method, where the patient executes themovement mentally. MI provides an active movement control by the patient and may includemore natural and voluntary motions. The approach is based on the fact that an activation of themotor system can be seen during both motor imagery and actual execution of a movement. [Par-sons, 2001], [Frak et al., 2001]Even though the use of new technological strategies to improve motor recovery after stroke hasexploded over the past two decades, there are still questions about the effectiveness versus thecost. The use of robotic devices and FES are undoubtedly technological revolutions, but they willremain passive methods when applied alone, since they are not able to integrate the patients ownwill easily. However, this can be done with motor MI. [O’Dell et al., 2009]

In the Center for Sensory-Motor Interaction (SMI) at Aalborg University a new rehabilitationstrategy is proposed based on a brain-computer interface (BCI). By using appropriate brain sig-nals, movement intentions of the stroke patient can be predicted in order to trigger the executionof the actual movement.This study aims on the development of a user friendly operating interface for a BCI system,whereby the signal processing of the applied brain signals is not in the focus of the report.

2 1. Introduction

PART

IPROBLEM ANALYSIS

3

4

Chapter 2

Neurophysiological backgroundPatients exposed to stroke usually have an impairment in the motor-, cognitive- and emotionalsystem. After acute stroke about 80 % of the patients have some form of motor impairment.[de Vries and Mulder, 2007] In order to help these patients regaining the lost motor function,neural and somatic structures which are involved in a movement control has to be identified. Thefollowing chapter will describe the road map for a thought of a movement in the brain to thedesired action of the limb. Due to its high complexity, the level of explanation is limited to therelevant parts.

2.1 Movement control

Skeletal muscles can be controlled from many places in the central nervous system (CNS), e.g.the spinal cord, the pons, the basal ganglia, the cerebellum and the motor cortex. Each part has itsown unique role.The spinal cord is mainly seen as a channel passing signals from the brain to the periphery ofthe body and the other way around. In reality the structure is more complex as the spinal cordconsists of several cord centers, which are commanded by the upper levels of the nervous system.These neuronal circuits in the cord are among others responsible for walking movements or dif-ferent reflexes. Pons, basal ganglia and cerebellum belong to the lower brain (subcortical level)and control automatic, instantaneous muscle responses to sensory stimuli. These muscle activitiesare also called subconscious activities and include for instance the control of arterial pressure andrespiration. The motor cortex (figure 2.1) is part of the higher brain (cortical level), which is incharge of complex movements that are controlled by thought processes. This part of the brain alsofunctions as a storage of information for future control of motor activities. [Guyton and Hall, 2006]

Figure 2.1: Overview of the cortical areas of the brain [Despopoulos and Silbernagl, 2003].

Almost all voluntary movements a person performs begin in the cerebral cortex and are based onmovements the person has done numerous times before. When a person wants to perform a certaintask, the cortical motor area starts a pattern already stored in deeper layers of brain stem, basalganglia, cerebellum or spinal cord and combines the movement with the information that comes

5

2.2 Voluntary movement control

from the somatosensory cortex (figure 2.2). The constant feedback from the somatosensory cortexenables adjustment of the voluntary movement before and during the execution. [Guyton and Hall,2006], [Despopoulos and Silbernagl, 2003]

As an important control center for motor function the cerebellum has afferent and efferent connec-tions to the periphery and the cortex. When a new unknown movement is needed (motor learning),the cerebellum (figure 2.1) is responsible for motor adaptation to the new movement sequence butalso associated in the planning, execution, controlling and refining of movements. [Despopoulosand Silbernagl, 2003]

Figure 2.2: Events from decision to move to execution of movement [Despopoulos and Silber-nagl, 2003].


A motor command, coming from the CNS, is delivered by the somatic nervous system (SNS)which controls the voluntary contraction of skeletal muscles. The somatic motor pathway whichenables voluntary skeleton muscle involves at least two types of motor neurons; lower motorneurons and upper motor neurons. The latter synapses on the lower motor neuron, resulting ina innervation of a single motor unit of a skeletal muscle. This means a destruction or damageof the lower motor neurons would eliminate voluntary control of the corresponding motor unit.The connection between the upper and the lower motor neurons is supported by the corticospinalpathway. When a voluntary movement is initiated, neurons in the M1 (figure 2.1) send commandsto upper and lower motor neurons.All neurons in the M1 are responsible for one specific somatic location respectively and arrangedin a somatotopic representation (figure 2.3). The leg components are in the middle, the facecomponents are situated laterally and the toes are represented deeply in the cortex. The largestrepresentations belong to muscles which imply the finest movements. [Guyton and Hall, 2006],[Despopoulos and Silbernagl, 2003], [Martini, 2006]

6 2. Neurophysiological background


Figure 2.3: Representation of the different muscles of the body in the motor cortex [Guyton andHall, 2006].

The M1 cannot initiate a movement alone, but needs to be stimulated by neurons from the premo-tor cortex and the supplementary motor area (SMA), which support and coordinate the M1. Onetask of the premotor cortex is to provide sensory guidance of movement while the SMA is, amongothers, responsible for planning and coordination of more complex movements, e.g. movementsthat require two hands. [Martini, 2006]The premotor cortex and the SMA are able to receive information from different decisional centerswithin the brain. These brain areas interpret the information and act according to it by coordinat-ing the execution commands, transferred to the M1, which subsequently sends the signal to theeffectors. [Martini, 2006]If for instance sensory information arrives at the CNS, it is routed according to location and natureof the stimulus. The receiving brain center, e.g. the M1 tells that an object is collected by the arm,which will initiate the movement pattern. Thus, the movement is the consequence of triggering thepattern instead of stimulating each neuron separately, which means the subject is able to performthe movement more easily when it is repeated. [Martini, 2006], [Despopoulos and Silbernagl,2003]

2.2.1 Overview of the nervous system

When the desired motor command is issued by the CNS, it is distributed to the destined limb by theperipheral nervous system. The peripheral nervous system is divided into an efferent division andan afferent division, where the efferent division carries motor commands from the central nervoussystem to the effectors. The efferent system is further divided into the SNS and an autonomicnervous system (ANS). The SNS controls voluntary skeletal muscle contractions, where the ANSis responsible for automatic regulation of e.g. smooth muscles and cardiac muscles. The afferentdivision brings sensory feedback from the peripheral tissues back to the CNS. In this way a closedloop system is established. [Martini, 2006]

Sensory feedback coming from the periphery, enters the spinal cord through the dorsal roots ofthe spinal nerves. The signals from the skin and locomotor system are then carried through one of

2. Neurophysiological background 7


two alternative sensory pathways up to the brain, the dorsal column-medial lemniscal system orthe anterolateral system. Both signal tracts are partially combined again in the thalamus.

Figure 2.4: Transmission of sensory information through the dorsal column-medial lemniscalsystem or the anterolateral system to the somatosensory cortex (S1) [Despopoulosand Silbernagl, 2003].

The dorsal column-medial lemniscal system (figure 2.4) consists of large fibers that allow signaltransmission with high velocities. Furthermore it has a high degree of spatial orientation of nervefibers in relation to their origin. These properties makes the dorsal system appropriate for carryingsignals which have to be transmitted rapidly and with temporal and spatial accuracy, for instanceposition sensations from the joints. On the contrary, sensory information that does not have tobe transmitted rapidly or with high accuracy is carried mainly by the anterolateral system. How-ever, the anterolateral system has the possibility to transmit a wide range of sensory modalities,such as pain, thermal sensations and crude tactile sensations. Similar to the motor cortex (figure2.3), the different parts of the body are represented in the somatosensory area. [Despopoulos andSilbernagl, 2003], [Guyton and Hall, 2006]

2.2.2 Skeleton muscles

When the motor neuron reaches the skeleton muscle, it splits into collaterals with terminal branches,which supply 25-1000 muscle fibers. When a skeleton muscle receives an action potential (AP),the muscle will contract. If one AP arrives immediately after another, they will accumulate. Inthis manner the first stimulus facilitates the response to the second stimulus, which increases themuscle strength. [Despopoulos and Silbernagl, 2003]


2.3 Movement impairment in stroke patients


Patients exposed to strokes usually have an impaired motor, cognitive and emotional system, dueto a lesion in the corresponding cortex. Whereas 80 % of the patients have some form for motorimpairment, 20 % regain a part of their motor control, leaving 60 % with a chronic motor disorder[de Vries and Mulder, 2007]. The movements are still decided in the prefrontal cortex and sendto the motor cortex and the basal ganglia, but the movements cannot take place, due to cell deathcaused by the stroke. [Martini, 2006].

2. Neurophysiological background 9



Chapter 3

Stroke rehabilitation approachesThis chapter focuses on different methods of stroke rehabilitation and explains the underlying ac-tive principles. The methods reach from physiotherapy and constraint-induced movement therapywhich work without the help of machines, over to the application of robotic therapy and electri-cal stimulation. Finally motor imagery related to imagination and observation of movements isexplained.

3.1 Physiotherapy

Physiotherapy in stroke rehabilitation aims on the establishment of a certain physical basis onwhich future functionality can be build upon with the goal of independence in the far future.Thereby, physiotherapy mainly focuses on the performance of controlled and balanced move-ments in order to enhance strength and stamina and increase mobility. [of Malaysia, 2011]Physiotherapy treatment starts directly after a stroke occurred. First, the physiotherapist will eval-uate the degree of disabilities in order to develop an appropriate rehabilitation plan. During thecourse of treatment the physiotherapist will assess to which extend recovery of the lost limb controlcan be reached in comparison to the state before stroke. If permanent impairment is conceivableit is the task of the physiotherapist to teach the patient methods how to manage daily life withoutthe affected limb. [Hendersen, 2011]

3.2 Constraint-induced movement therapy

In constraint-induced movement therapy (CIMT) the stroke patient is forced to use the affectedside of the body by immobilization of the unaffected side. CIMT was developed by the Americanpsychology professor Dr. Edward Taub. He observed that stroke patients evolve habits wherethey do not use the impaired limb, especially in the early phases after the event. Instead theylearn techniques to compensate the disability by an increase use of the intact limb, which leads toprohibition of functional recovery in the impaired limb. [Grotta et al., 2004]When applying CIMT today, e.g. on a patient whose left arm is impaired, the therapist constrainshis right arm in a sling. In this manner, the patient is forced to use his affected arm repetitivelyand intensively. A requirement for treatment according to CIMT is the patient’s existing abilityof wrist extension and arm and finger movements. It is also proven that CIMT enhances brainactivity. [Taub et al., 2000]

3.3 Robotic therapy

Frequent therapeutic rehabilitation training for stroke patients have shown to be effective in re-learning of motor skills [Kwakkel et al., 1997]. However, due to finite physiotherapy resources,the patient can not invest enough time in concentrating on rehabilitation activities. This leads tothe approach of amending traditional physical therapy with robot assisted therapy [Jackson et al.,2007].In general, a robotic device supplies the intended movement of a patient by mimicking the role ofthe physiotherapist. For instance, for rehabilitation of functions of the arm, the patient is graspinga joystick-like handle which is connected to the computer monitor in front of him (figure 3.1).

11

3.4 Functional electrical stimulation

A suggestive task could be to carry out a particular movement, e.g. moving the handle towardsa fixed or moving target displayed on the monitor. If the patient moves the handle in the wrongdirection the robotic device will take over control in order to correct the direction. Furthermorethe device will try to initialize the move if no movement happens at all by slightly nudging thearm of the patient [Trafton, 2010].

Figure 3.1: Robotic therapy; the patient tries to follow with the handle a target which is displayedon the screen [Trafton, 2010].

3.4 Functional electrical stimulation

Functional electrical stimulation (FES) is the electrical stimulation of nerve pathways to produce acontrolled contraction of muscles with the purpose of imitating normal voluntary movements andrestoring neuroconduction function [Hewitt, 2011], [Raymond, 2006].Cortical plasticity is a fundamental factor which influences motor relearning when FES is usedrelated to stroke rehabilitation [Jenkins and Merzenich, 1987], [Alamancos et al., 1992], [Pieroet al., 1992], [Chae and Yu, 1999], [Ridding and Rothwell, 1999]. When learning a motor task thecortical excitability increases. Further development happens when the movement is repeated andgrows significantly during skillful training tasks [Muellbacher et al., 2001], [Nudo, 2003], [Nudo,2007], [Perez et al., 2004]. Peripheral electrical stimulation (PAS) plays a dominant role in motorrelearning in order to improve disabled functions [McDonnell and Ridding, 2006], [Powell et al.,1990].A simple FES system (figure 3.2) consists of a main unit, a stimulator, a control sensor and stim-ulation electrodes. The stimulation electrodes are placed on the target muscle. The sensor is usedto control the electrical pulse from the main unit and detect the intensity feedback of the patient.Generally the electrical pulse ranges from 150 µs to 300 µs, while the stimulation amplitude rangesfrom 10 mA to 150 mA and the stimulation frequency between 20 Hz and 40 Hz [Raymond, 2006].

12 3. Stroke rehabilitation approaches

3.5 Paired associative stimulation

Figure 3.2: The components of a simple FES system [Peckham and Knutson, 2005].

Three types of training were designed in a finger flexion/extension experiment to test the best wayfor motor relearning; 1) FES alone, 2) voluntary muscle activity alone, 3) therapeutic FES (TFES)- the combination of voluntary movement and FES. It was concluded that TFES is better thanthe other two methods because more obvious changes were produced on motor evoked potential(MEP) area. Besides TFES had a great potential to restore neuroconduction function efficiently.[Barsi et al., 2008]

3.5 Paired associative stimulation

Paired associative stimulation (PAS) is a protocol that uses the Hebbian principle (appendix A) bystimulating the median nerve with a slow-rate repetitive low frequency followed by a transcranialmagnetic stimulation (TMS) (appendix B) on the contralateral motor cortex. Due to the theory ofHebbian, the synaptic efficacy induced by associative activity is dependent on the order of activityfrom the pre- and postsynaptic neuron. When the postsynaptic cell is depolarized synchronouslywith the afferent input from another cell this will result in a Hebbian long-term potentiation. Incontrast if the postsynaptic neuron spiked before excitatory postsynaptic potential was induced byafferent stimulus, it will result in a long-term depression which is also called asymmetric Hebbianrule.TMS is a noninvasively technique to stimulate peripheral nerves and brain tissue by using the prin-ciple of electromagnetic induction. When timed appropriately, PAS induces a persistent increasein corticospinal excitability which could be demonstrated as an indicator of plasticity within M1[Cirillo et al., 2009], [Classen et al., 2004]. It is possible to induce an increase in amplitude ofthe MEP when TMS is transferred at the intensity below the demand of evoking a response in aresting target muscle by using PAS. The increase is conditionally dependent on the timing betweenthe afferent and the magnetic stimulation which needs to be synchronous at the level of the motorcortex. A study showed that the plasticity changed rapidly within 30 minutes by inducing PAS andhad a duration of minimum 60 minutes [Stefan et al., 2000], [Jayaram et al., 2007].Studies have shown that stimulation of peripheral nerves, peripheral nerve and motor cortex ormuscle motor point and motor cortex is capable of inducing reorganizations within the human mo-tor cortex in association with skill acquisition [Pascual-Leone et al., 1994], [Stefan et al., 2000],[McKay et al., 2002], [Ridding and Uy, 2003].The dominate theorem is that the organizational changes induced by modifying afferent input are

3. Stroke rehabilitation approaches 13

3.6 Motor imagery

similar in nature to those seen during motor learning. This is already used in order to induce ther-apeutically relevant plasticity [Conforto et al., 2002], [Fraser et al., 2002].In relation to stroke patient PAS can induces a modulation in the corresponding non-paretic limband reduce the asymmetry between the the healthy and the impaired limb. This means cortico-motor excitability decreases in non-lesioned motor system but increases in lesioned motor systembetween hemispheres with inhibitory PAS in imperative or voluntary movement and in rest [Stin-ear and Hornby, 2005], [Rogers et al., 2010].

3.6 Motor imagery

Improvements of stroke rehabilitation is among others dependent on the amount of sensory feed-back given by motor activity [Kwakkel et al., 2004]. There are five commonly known sources ofsensory information [de Vries and Mulder, 2007]:

• Tactile information• Vestibular information• Proprioceptive information• Visual information• Auditory information

Recent results suggest that imagination and observation of movements could produce a furthersource of information that could be appropriate in stroke rehabilitation. A study by Feltz andLanders stated that subjects who where mentally practicing a particular task showed better resultsin the performance than subjects who did not train at all. However less improvements were seenin comparison to physical practice. [Feltz and Landers, 1983]The term MI can be defined as the mental process of imagining a certain body movement withoutmoving it in reality [Jeannerod, 2001], [Mulder et al., 2005]. Two different perspectives of MIcan be distinguished. In the first person perspective, the person thinks about the feeling of themovement itself. In comparison, the third person perspective describes the situation when theperson is imagining another person doing the movement. The latter is more a visual imaginationthan feeling. To enhance relearning of a new skill, the first person perspective is appropriate whileinvolving in learing coordinate and timing of motor skill and the third person perspective couldplay an important role in motor tasks focusing on the form of movement.[de Vries and Mulder,2007]

3.6.1 Evidence for the use in rehabilitation

In the field of sports sciences it was shown that learning of a movement is supported by the repeatedfirst person perspective imagery [Feltz and Landers, 1983], [Driskell et al., 1994]. An importantcondition for the application of MI is that it is only appropriate if a representation of the movementexist, meaning that the subject must somehow have the ability to act. However, there are only fewstudies dealing with MI in terms of rehabilitation of stroke patient. The first arbitrary controlledstudy was made by Page et al. with early stroke patient (2 to 11 months post-stroke). It could beproven that the patient who besides physical therapy also received motor imagery training showeda significant higher improvement in comparison to the control group. The control group got somegeneral information about stroke instead. [Page et al., 2001] In a second study Page et al. foundthe same result even for chronic stroke patient (one year post-stroke)[Page et al., 2005].


3.6 Motor imagery

3.6.2 Evidence for similarities in cortical activation

The approach of stroke rehabilitation through motor imagery is based on substantial similaritiesbetween motor imagery and the actual execution of a movement. Studies by Parsons and Frak etal. showed that the duration for mentally executing a movement is quite similar to the time whichis needed to carry out the actual movement [Parsons, 2001], [Frak et al., 2001]. This observationis called mental isochrony.A further evidence for similarity is based on studies using brain-mapping techniques [Jacksonet al., 2001], [Jeannerod, 2001]. It was found that the same brain areas are activated during imagi-nation and the real action. In particular these are the parts of the neural system which are associatedwith preparing and commanding of movements: the premotor cortex, the dorsolateral prefrontalcortex, the inferior frontal cortex, the posterior parietal cortex, the cerebellum and the basal gang-lia. The activation of the M1 during imaginary movements is still unclear because some studiesfound neural activation and others not. This is however, a crucial question due to the conclusion ofseveral studies that neural reorganization concerning motor re-learning happens in the M1. Espe-cially the latter mentioned correlation is an important observation as it states that the point in timecan be identified when the impaired subject wants to execute a movement. [Hlustik and Mayer,2006].

3.6.3 Mirror neurons

A further considerable indicator for possible ”offline” activation of the motor system was the dis-covery of mirror neurons, which become active, e.g. when a person is observing another personperforming a task [Rizzolatti, 2005]. Neurones related to goal-directed motor control were firstfound by Rizzolatti [Rizzolatti et al., 1987] in Marque Monkeys. Later [Fadiga et al., 1995] con-cluded that these kind of neurones must somehow also exist in the human motor cortex. Duringtheir experiment they could see an increase in MEP when the subject observed another persongrasping a object. Furthermore, the shape of the recorded MEP reflect the shape of MEP, whichwas recorded when the subject carried out the movement itself. Due to these findings researchersconsider mirror neurons to play potentially a role in the re-learning of motor control [Gallese,2005]. However, the function of mirror neurons is not completely understood yet, which may beone reason why there are no systematic studies in this subject area of neurological rehabilitation[de Vries and Mulder, 2007].

3. Stroke rehabilitation approaches 15

3.6 Motor imagery


Chapter 4

Rehabilitation with brain-computer interfacesystems

Brain-computer interface technologies are frequently mentioned in relation with therapeutic pur-poses. The use of brain-computer interface for inducing neural plasticity in order to rehabilitatestroke patient has gained more attention. The opportunity of using this method will be explainedin this chapter.

4.1 Brain-computer interface system

A brain-computer interface (BCI) provides a connection between humans and external devices orapplications by using neurophysiological signals obtained from the brain. There are two differenttypes of BCI systems, invasive- and non-invasive. Invasive BCI is directly implanted in the braintissue by using electrodes, while non-invasive BCI makes use of electrophysiological recordings.Electroencephalography (EEG) (appendix C) is commonly used as a recording technique for non-invasive BCI systems. [Birbaumer and Cohen, 2007]

BCI systems let the user convert thoughts into actions, which do not involve voluntary musclemovement. The system offers new means of communication for people with paralysis or severeneuromuscular disorders and can be useful for patients whose natural output pathways of periphe-ral nerves and muscles are out of function e.g. caused by stroke. [Dornhege et al., 2007]

There are two separate approaches for control of BCI:

• The human learns to adjust a certain brain activity by getting a neurophysiological feedback.During training sessions the subject improves the ability to activate voluntarily different ar-eas in the brain, which can be utilized later as control commands.

• The machine learns to interpret the brain activity from the human using a learning algorithm.This will require adjustment (calibration) of the algorithm by asking the individual user togenerate a specific brain state several times. Based on these examples the machine deriveslater the statistical structure of specific brain activity.

In fact, in practice it is not possible to see the mentioned interactive learning algorithm completelyautonomous. There will always be a mutual influence between the learning user and algorithmwhereby it is still an open question how they can be combined ideally. [Dornhege et al., 2007]

4.2 Inducing neural plasticity by using brain-computer interface

Research in BCI has been mainly concentrated on providing alternative communication devicesfor the past decade. This has changed in the recent years due to a increasing interest in extendingthe application range of BCI technology. One of the most interesting new approaches is the use ofBCI technology to induce neural plasticity, and thereby restore damaged brain functions. [Grosse-Wentrup et al., 2011]

17

4.3 Brain-computer interface based on movement-related cortical potentials

Studies on attention deficit hyperactivity disorder (ADHD) and epilepsy has provided proof-of-principles for a positive impact of BCI technology in inducing cortical reorganization, and strokepatient have been shown capable of operating a BCI based on magnetoencephalography (MEG)and electrocorticography (ECoG). [Grosse-Wentrup et al., 2011]

BCI can complement the existing rehabilitation methods by detecting specified actions in the brainsignal, and thereby provide feedback to the patient. This can induce cortical reorganization due toneural plasticity and cause functional recovery. A well-known approach of this principle is basedon the theory of Hebbian (appendix A). Assuming the case of patient which is suffering from asub-cortical stroke, the connection between peripheral muscles and the sensorimotor cortex hasbeen corrupted. If it would be possible to activate sensory feedback loops and M1 synchronously,formerly passive cortical connections can be strengthened by Hebbian plasticity. For this particularcase the task of the BCI system would be to detect M1 activation e.g. for an intended movement.When M1 activation is detected the BCI provides coordinated sensory stimulation. The sensorystimulation can be optimized to provide feedback on those neural states that are optimal for induc-ing neural plasticity. [Grosse-Wentrup et al., 2011]

4.3 Brain-computer interface based on movement-related cortical po-tentials

BCI technology might increase the efficacy of a rehabilitation protocol and thus improve the mus-cle control for stroke patients and others suffering from lost motor control. This can be imple-mented by detecting the planning phase of a movement in the brain activity and supplement thepatient’s impaired muscle control, e.g. send trigger (figure 4.1). The planning phase of a move-ment can already be measured as part of MRCP (appendix D) which are correlated to voluntarymuscle contraction and changes in the cortical activity over the area of the motor cortex. [Dalyand Wolpaw, 2008]

Figure 4.1: BCI system which is able to detect MRCP online from asynchronous (spontaneous)imaginary movements. The intended movement will be triggered by an electricalstimulation.

18 4. Rehabilitation with brain-computer interface systems


MRCP represent averaged EEG activity in the motor cortex off the brain related to a voluntarymovement [Yom-Tov and Inbar, 2003]. The signal is produced by firing of the neurons withinthe brain, here especially in the motor cortex, which produces an electrical activity that can berecorded as EEG from the scalp. It is not possible to detect the electrical potential of one singlefiring neuron due to the low amplitude of the discharge of the neuron. Therefore the EEG thatgets picked up on the scalp is a summation of thousands synchronous firing neurons. [Misulis andHead, 2003]MRCP may be a suitable approach for rehabilitation of stroke patients, because its morphologyhas been investigated under several different conditions. The phases of MRCP consist of an initialnegative phase (INP), also known as the early and late Bereitschaftspotential, and a movement-monitoring potential. The INP is considered to represent the planning phase of a movement and isbelieved to occur even if the movement is imagined.do Nascimento investigated the influence of the rate of torque development and torque amplitudeon MRCP during imaginary and real isometric plantar flexions in two different studies [do Nasci-mento et al., 2005] [do Nascimento et al., 2006]. In both cases it was concluded that informationabout these two factors are encoded in the MRCP. The comparison of MRCP from real movementsand imagined movements showed that the latter was mainly characterized by a reduced amplitude.Furthermore, although the known movement related parameters can be detected before the move-ment onset, the shape of the MRCP during imagination looked different. This applies especiallyto the part after the movements onset which is probably due to the disparity in efferent feedback.Due to the similarities between imaginary and real movements a BCI system based on MRCPmight be useful in order to detect intended movements [Cunnington et al., 1996].

4. Rehabilitation with brain-computer interface systems 19


20 4. Rehabilitation with brain-computer interface systems

Chapter 5

Problem formulationAn increasing number of individuals suffer from motor impairment caused by stroke. While dif-ferent rehabilitation strategies have been attempted for recovery of motor functions none of themhave provided significant improvements. [Birbaumer et al., 2008]

MI has been found to activate the same cortical areas as they are activated in real movementsexcept for the somatomotor cortex. This could be interpreted in this manner that both motor ex-ecution and MI have a similar movement preparatory phase, whereby the latter is not followedby execution commands from the motor neurons in the M1. MRCP opens important perspectivesin the development of new rehabilitation technology, because they allow to distinguish movementintentions, which can be used to control an output variable in BCI.

The current BCI system from the Center for Sensory-Motor Interaction (SMI) at Aalborg Univer-sity is able to determine asynchronous movements onsets offline. A true positive rate (TPR) forthe detection of INP was 63.3 % for healthy subjects during imaginary movements (n = 7; 63.3 %± 9.8 %) [Niazi et al.].

When combining the BCI system with peripheral electrical stimulation the system has to be ableto detect asynchronous movements online. Thereby one of the biggest challenges is the fast andaccurate detection of INP, which appears prior the actual movement onset. In order to do so, thiswill require a detection algorithm with minimal processing time to reduce feedback delay, whichis the time difference between the measurement of neural state and the response of the system.Further for the detection algorithm advanced signal processing techniques is required in order toobtain a maximal TPR of detection. The acquisition of an accurate feedback is important to induceneural plasticity. The latency between detection of INP until the triggering of the intended move-ment by electrical stimulation has to match the time that it takes from the onset of an INP untilthe actual movement is carried out by a healthy subject. Neural plasticity based on the Hebbianprinciple can be induced with a maximum latency of about 200 to 300 ms [Grosse-Wentrup et al.,2011]. The detection of a motor preparation phase such as INP will allow to predict the intentionto move in order to establish such a causal association. [Niazi et al.]A solution of these challenges, implemented in a rehabilitation program for stroke patients, willgive the patient a feeling of free will in aspect of movement control, because of the immediateelectrical stimulation after the intention of a movement. This approach might be an improved re-habilitation method compared to established rehabilitation therapies.

As a result of the problem analysis the following problem can be stated:

”How can a BCI system, based on online detection of INP, be designed and implemented so that

it can be used for rehabilitation purposes in order to help stroke patients regaining their basic

motor functions?”

21

22 5. Problem formulation

PART

IIPROBLEM SOLVING

23

24

Chapter 6

RequirementsBased on the problem formulation this chapter contains an overall description of the system withthe corresponding functional and non-functional requirements. The specific functional require-ments for the login screen, training mode, detection algorithm and testing mode are describedindividually.

6.1 System description

The system under development is the initial approach of making BCI work in a rehabilitation set-ting for stroke patients in order to help them regaining their basic motor functions. The systemneeds to be an online BCI which operate by trying to build the best possible detector in order toidentify INP from the subject’s EEG. Because each subject is unique, the detector has to be buildbased on individual recorded training data. When the detector has been found for the subject,it would be used in a future rehabilitation program to give electrical stimulation to the impairedextremity.

System overviewStart system (Login)

Detection algorithmTraining mode

Trigger (stimulation)

Testing mode

Ed it i

Figure 6.1: The BCI system can be mainly be divided into training and testing mode. A detectoris build from recorded training sessions. In the testing mode the detector is used todetect INP which can trigger an electrical stimulation.

The BCI system can be divided into two parts; a training mode and a testing mode (figure 6.1). Inthe training mode brain activity will be recorded from multiple EEG channels together with EOGand EEG from the tibialis anterior muscle. The parameters that has to be set before recording caneither be synchronous or asynchronous and real or imaginary movements. The stored recordingswill be used for building a detector which can detect INP online. In order to optimize and adaptthe detector, different training sessions from the database can be selected, which do not necessarilyhave to origin from the same subject.In the testing mode the detector is tested on imaginary synchronous or asynchronous movements.Every time an INP is detected, the intended movement of the subject will be triggered by anelectrical stimulation. The user is able to see the detection performance online through feedbackfrom the subject coded in finger movements.

25

6.2 General requirements specifications

6.2 General requirements specifications

The requirements are divided into functional and non-functional requirements. The functionalrequirements describes directly what the system should do, while the non-functional requirementsdescribes the limitations to be respected by the system.

6.2.1 Functional requirements

• The system will record nine channel EEG measured from the scalp.• The system will record one channel EOG measured from the eye.• The system will record three channel EMG (one channel from the tibialis anterior muscle

and two channels from the fingers).• The system will store all recordings locally.• The system will display EEG and EMG signals online.• The user will have the possibility to display one of nine recorded EEG channels at a time.• The system will give visual feedback to the patient about movement performance.• The system will send a trigger when an INP is detected.

6.2.2 Non-functional requirements

• The system must be programmed in MATLAB.• The system must be easy to use and the graphical interface must have a user friendly design.• The system will use hardware components, e.g. DAQ card, in order to acquire signals.

6.3 Individual requirements specifications

The system consist of a login window, a training and a testing part with a menu for building thedetector. Each part has its own requirements which have to be compiled in order to run the system.

6.3.1 Login window

The user have to enter the name of the subject in order to gain access to the training and testingmode.

• The user will have to enter a subject name before starting a session.• The system will check if the entered subject name exists in the database.• The system will continue to add new training and testing sessions to the existing subject if

the name exists.

6.3.2 Training mode

In the training mode the user can choose to record sessions based on four different movementparameters.

• The system will have following parameters; synchronous or asynchronous and imaginary orreal.

• The system will show two windows containing EEG and EMG signals online.

26 6. Requirements


• The system will provide visual feedback to the subject by showing a subject window onan external screen containing the force of EMG movement and other relevant informationspecified by the user.

• The system will save the selected parameters and recorded data.

6.3.3 Detector menu

The recorded sessions in the training mode will be used to build a detector that is able to detectINP online in testing mode.

• The system will build a detection algorithm based on specific training sessions, which areselected by the user.

• The algorithm will be able to detect INP online in testing mode.• The accuracy will be based on a threshold of TP and FP selected by the user.• The system will store the detection algorithm until a new is build.

6.3.4 Testing mode

The testing mode will use the detection algorithm for detecting INP online.

• The system will have following parameters; synchronous or asynchronous.• The system will show two windows containing EEG and EMG signals online.• The user will have the possibility to turn on/off the electrical trigger.• The performance of the used detector will be displayed during the testing mode.• The system will provide visual feedback to the subject by showing a subject window on

an external screen containing the performance of the test and other relevant informationspecified by the user.

• The system will save the selected parameters and recorded data.

6. Requirements 27


28 6. Requirements

Chapter 7

DesignThis chapter explains the design of the system based on activity diagrams, which is also known asobject-oriented flowcharts. The purpose of an activity diagram is to give a graphical representa-tion of where and at which time the stepwise actions and activities in a program are taking place.This is visualized in a flowchart.

7.1 Design of the graphical user interface

The system is divided into four main components; login window, training mode, detector menuand testing mode.

7.1.1 Login window1. Login screen

User System

Start program

Type name

Confirm name

Error message

Validate name

Enable training mode and

test ing modeChoose mode

Open test ing modeOpen training mode

[Training mode is choosen]

[Test ing mode is choosen]

[Name entered or already ex ist ]

[Name is not entered]

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py]

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Figure 7.1: Activity diagram for the login window.

When the program starts, the user first has to login the name of the subject (figure 7.1). The inputwill be confirmed by the user and subsequently checked by the system. If the entered name isvalid, the system will allow the user to select between training mode and testing mode. Otherwise,if the input is invalid the system will return an error message prompting the user to repeat theprocess.

29


7.1.2 Training modeActivity Diagram

2. Training modeUser System

Choose training pattern

Set subject window

Press startSave sett ings, init ializesignal acquisit ion andopen subject window

Start recording Recording

Stop recording

Check subject name indatabase

Discard recorded sessionSave recorded session

Validate session

Build detector

[Preparing trainingsession is choosen][Build detector is choosen]

[Save session is choosen]

[Discard session is choosen]

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Figure 7.2: Activity diagram for the training mode.

The training mode (figure 7.2) will start, when the user has selected it after login of the subject.Before starting a training session, the user has to choose between four different movement param-eters; real or imaginary movements and synchronous or asynchronous movements. Furthermore,the user can decide which information should be presented to the subject as visual feedback. Theseelements are; a bar which reflects the EMG signal, duration of the current recording and a count-down timer which is used for synchronous movements.After all settings are made the user starts the data acquisition. The incoming signals from EMGand EEG channels are visualized to the user. If the user decides that the signals are up to thestandard a recording can be started.When the user stops the recording the system will prompt to decide whether the current sessionshould be saved in the database. If the user does not want to save the data, the current session will

30 7. Design


be discarded. Otherwise the data will be saved. At this point one training session is finished andthe user has the possibility either to build the detector or to start a new training session.At all times when the user has the control, it is possible to finish the training mode and switch backto login. Therefore this activity is neglected in the activity diagram.

7.1.3 Detector menuActivity Diagram

3. Detection algorithmUser System

Select subject

Select sessions and enter

used gain

Start building detector Extract MRCP- template

Enter threshold for EOG

activity

Enter TP and FP

Building ROC- curve

Finishing detector

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Figure 7.3: Activity diagram for the detector menu.

The user can build the detection algorithm (figure 7.3) either after finishing training mode or beforestarting testing mode. At the beginning the user has to select the training sessions to be includedfor the composition of the detection algorithm.When the training sessions are selected the user has to state the gain of the EEG signals used forthe recording. Then the preprocessing of building the detection algorithm can be started. In thenext step the user has to state the threshold for the eye electrode (EOG channel) based on a graphwhich shows the energy at the eye electrode in each epoch of the selected sessions. If the energyis too high it will disturb the MRCP signal in this epoch and thereby also influence the detectionalgorithm. By setting a threshold these epochs will be excluded.Finally the user confirms the threshold and a receiver operating characteristic (ROC) curve isobtained. Based on this curve the user states a number for accepted true positive and false positive

7. Design 31


values. After confirming the input again the system pops up with a message, that the build of thedetector is completed.

7.1.4 Testing modeActivity Diagram

4. Testing modeUser System

Choose training pattern

Set subject window

Set trigger for st imulat ion

Press startSave sett ings, init ializesignal acquisit ion andopen subject window

Start recording

Stop recording

Recording (send triggerfor st imulat ion)

Validate session

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Figure 7.4: Activity diagram for the testing mode.

The testing mode (figure 7.4) will start when the user has build the detector. Before starting a test-ing session, the user has to choose between two different movement parameters; synchronous orasynchronous imaginary movements. Furthermore, the user can decide which information shouldbe presented to the subject as visual feedback. These elements are; a bar which reflects the EEGsignal from the selected channel, duration of the current recording and a countdown timer whichis used for synchronous movements.Unlike the training mode, the testing mode will use the detector for detecting the INP of MRCPonline. Whenever the INP is detected and the energy on the EOG channel is below the threshold,

32 7. Design


the intended movement of the subject will be supported by an electrical stimulation with low in-tensity. This stimulation is possible to toggle on and off.After all settings are made the user starts the data acquisition. The incoming signals from EMGand EEG channels are visualized to the user. If the user decides that the signals are up to thestandard a recording can be started.When the user stops the recording the system will prompt to decide whether the current sessionshould be saved in the database. If the user does not want to save the data, the current session willbe discarded. Otherwise the data will be saved. At this point one testing session is finished andthe user has the possibility either to build the detector or to start a new testing session.At all times when the user has the control, it is possible to finish the testing mode and switch backto login. Therefore this activity is neglected in the activity diagram.

7. Design 33


34 7. Design

Chapter 8

ImplementationThis chapter contains an in-depth explanation on how the system is developed and the functionswhich the system is build up upon. The functions will be explained with respect to the correspond-ing GUI.

8.1 Implementation of the graphical user interface

The system is divided into four main components; login window, training mode, detector menuand testing mode.

8.1.1 Login window

Figure 8.1: Screenshot for the login window

Features:

• Enter subject name• Verification of subject name• Select training/ testing• Quit the program

After the user has started the program a login screen will be presented prompting the user to en-ter the name of the subject in a text box (figure 8.1). The buttons for training and testing appeardisabled at the beginning. After pressing the OK button the system will check the input. In casethe entered name is valid the system will allow the user to select between training or testing. If theOK button is pressed without entering a name, an error message will pop up.

35


8.1.2 Training mode

Figure 8.2: Screenshot for the training mode

Features:

• Display of subject name and training session number• Display of EMG and EEG signal• Display of recording time• Set training patterns (movement type)• Set visibility of elements in subject window• Control of recordings

After the user has selected training from the login screen a new window will open representingthe training mode (figure 8.2). The subject name is displayed together with the session number inthe upper left corner of the window. If the entered name does already exist in the database, thesystem will automatically continue counting the training sessions according to the existing numberof previous saved sessions.In the upper right corner of the window the user can make the settings for the upcoming recording.The elements displayed in the subject window (figure 8.3) can be activated/ deactivated simply bypressing check buttons. The training patterns and the displayed EEG channel can be selected bydropdown menus. EMG and EEG are displayed in the center of this window.

36 8. Implementation


Figure 8.3: Screenshot for the subject window

By pressing the Start/Stop button data acquisition from the DAQ card is controlled. After Start ispressed the system assigns space for incoming data which is then continuously acquired from theinput channels. The input is stored temporarily in a stack structure. Besides, the subject windowpops up at the second screen.By pressing the Record button the acquired EEG and EMG signals are recorded and saved in afixed path. Moreover the subject window is continuously updated with the EMG signal and thetimer starts to count up in both the training and the subject window. During the recording the useris able to change the displayed EEG channel.When the recording is stopped the subject windows closes and the user is asked by the programif the current session should be saved. The session can either be discarded or saved in a separatefolder for training sessions. The latter case entails to increment the training session number byone. The Detector button directs the user to the detector menu. When pressing Finish training theprogram goes back to the login screen.In the subject window (figure 8.3), the visual feedback reflects the force of EMG movement in thetraining session, but in the testing session, it presents the output of matched filter.

8.1.3 Detection algorithm

Figure 8.4: Screenshot for the detector menu

8. Implementation 37


Features:

• Specification of subject selection• Listing of training sessions• Set amplifier gain• Display MRCP templates and EOG activity• Set threshold for EOG activity

In the detector menu the user selects the training sessions to be included for the composition ofthe detection algorithm (figure 8.4). In order to simplify the search the user first can make apreselection, deciding whether to choose sessions only from the logged in subject or from all sub-jects. After training sessions are selected from the listbox the user has to enter the gain used forthe recorded signals.The first graph is to calculate the averaged signal from channel Cz, the second one is the tem-plate which is illustrated detailed in the following paragraphs and the third one is the power ofEOG in each EEG 3 s epoch. The process of finding the best possible detection algorithm can bedivided into two steps. In the first step a spatial filter is applied to the training data to obtain asurrogate channel from the EEG channels recorded. From the surrogate channel MRCP templateis extracted. In the second step this template is used to detect movement onsets the training databy a matched filter algorithm.

The first step is started by pressing Build detector. The signal processing behind is describedbelow. EEG signals are loaded, bandpass filtered from 0.05 Hz to 10 Hz and downsampled from500 Hz sampling frequency to 20 Hz. Then a large Laplacian spatial filter (LLSF) is appliedin order to obtain a surrogate channel (linear combination of the nine channels) from the EEGchannels because LLSF provides a global linear filter resulting in a more concentration on thesignal which contributes the most [Mellinger et al., 2007]. The channel coefficients are calculatedas the following:

xk =

1, i = 1− 1

(Nch−1) , i = 1(8.1)

where Nch is the number of channels. The Cz-channel corresponds to the highest weight one andthe other channels have a weight of −1/8, so that the sum of all channels is equal to zero. Thisapproach blocks direct current interference effectively. The surrogate channel now contains therecorded data.

In the next phase the 3 s epoch of signals are abstracted and divided into 2 s before and 1 s aftereach event which is defined as the indication of movement occurence implying the power of EMGis above the 10 % of the maximal value.

In case of synchronous imaginary movements the epochs corresponds to the periodic cue and forasynchronous imaginary movements the subject is asked to give an indication by pressing a button2 s after the event.

For each 3 s epoch, in order to increase the SNR of the EEG signals, an optimized version of theLLSF called optimized spatial filter (OSF) is applied. OSF uses the coefficients obtained fromLLSF and aims on finding the optimal combination of EEG channels that maximizes MRCP en-ergy while minimizing the noise energy. Since the used filter technique is a data driven approach,



EEG channels that contain the more signal information have to have a higher weight than others.By getting an optimal set of x, while the sum of the coefficients is still zero, SNR will be maxi-mized. The average algorithm is applied for all the epochs to get the 2 s long template.

Eye blinking during a session produces large artifacts in the EEG channels which influences theMRCP template. For that reason electrooculography (EOG) activity is calculated for each epoch.The results are plotted as power of EOG in the third graph of the detector menu. According to theliterature an epoch with EOG activity above 125 µV should be rejected. However, in the detectormenu the user can choose the threshold manually according to the individual requirements.By pressing Cross validation the second step of finding the optimal detector is started. The ex-tracted template is used to detect movement onsets in the training data. A detection decisionunderlies the likelihood ratio method and is computed (2 s sliding window with 200 ms shift) be-tween the surrogate channel based on the training data and the obtained MRCP template. Fromthis cross validation the user will be presented to a ROC curve in a separate window. The ROCcurve represents a graphical plot of the sensitivity, given by the truth positive rate vs. false positiverate. The user picks a percentage value, by aiming for the best relation between a high true positivevalue and low false positive value, and input these values. Normally, the middle point of turningphase in the ROC curve is adoptive.

8.1.4 Testing mode

Figure 8.5: Testing mode

Features:

• Display of subject name and testing session number• Display of EMG and EEG signal• Display of recording time• Set visibility of elements in subject window• Set testing patterns (movement type)• Set electrical stimulation on/off• Control of recordings



In order to attain the testing mode the user has to build a detection algorithm beforehand. Subse-quently the window, called testing mode will open (figure 8.5). It can be seen that the window isvery similar to training mode(figure 8.2). The differences or enhancements are explained in thefollowing. In the upper right side of the window the user can decide if the recorded movementswill be synchronous or asynchronous. Furthermore the electrical stimulation can be activated/ de-activated simply by pressing radio buttons.

By pressing the Start/Stop button data acquisition from the DAQ card is controlled. After Start ispressed the system assigns space for incoming data which is then continuously acquired from theinput channels. The input is stored temporarily in a stack structure. Besides, the subject windowpops up at the second screen.By pressing the Record button the acquired EEG and EMG signals are recorded and saved in afixed path. Moreover, the subject window is continuously updated by the EEG signal from thechannel and the timer starts to count up in both, the testing and the subject window. During therecording the user is able to change the displayed EEG channel.A detection decision underlies the likelihood ratio method and is computed (2 s sliding windowwith 200 ms shift) between the surrogate channel from the recorded testing data and the MRCPtemplate. A movement is detected online when two consecutive windows cross the threshold ob-tained from the ROC curve. If, in addition, the power of EOG activity lies in the allowed range of0-125 µV a command for electrical stimulation is send.When the recording is stopped the subject windows closes and the user is asked by the programif the current session should be saved. The session can either be discarded or saved in a separatefolder for testing sessions. The latter case initiate an increment for the testing session number byone.The Detector button directs the user to the detector menu. When pressing Finish testing the pro-gram goes back to the login window.

The performance of the current detector is displayed by a truth positive rate (TPR), a false posi-tive rate (FPR) and a false negative rate (FNR). The performance is based on a feedback given byfinger movements from the subject. The three different cases are detected in the following way:

Case 1: Truth PositiveThe subject is instructed to extend its right index finger when a peripheral electrical stimulation isgiven right after a movement is executed (stroke patient) or imagined (healthy subject). This willmean that the detector has detected the INP in the EEG virtual channel.

Case 2: False PositiveThe subject gets an peripheral electrical stimulation although no movement is executed (strokepatient) or imagined (healthy subject). This will mean that the detector has mistakenly detected aINP in the EEG surrogate channel which did not occur.

Case 3: False NegativeThe subject is instructed to extend its left index finger when a peripheral electrical stimulation isnot given right after a movement is executed (stroke patient) or imagined (healthy subject). Thiswill mean that the detector has not detected the INP in the virtual channel.

The finger movements are detected by measuring EMG on first dorsal interosseus muscle fromindex finger of the left and the right hand. Two to three seconds after starting data acquisition



the user has to press Calibrate. This will prompt the calculation of the threshold used for fingermovement onset. During the recording the EMG signals are bandpass filtered from 30 Hz to 250Hz, rectified and downsampled to 20 Hz. The occurrence of a finger movement is defined as theexceedance of the threshold by five consecutive samples.




Chapter 9

TestingBased on the developed software this chapter contains the tests that are performed during thedesign and implementation of the system and the subsequent tests with the finished system. Thechapter is divided in two parts, where the first part will concentrate on the testing that was doneduring the development of the program, while the last part will focus on the testing of the finishedprogram.

9.1 Tests of the developing system

Continuous testing of the system is a natural way of developing a program. In this manner itis possible to locate and mend errors to make the program run correct. The test of the systemwas performed according to Unified Process, which contains following tests [Arlow and Neustadt,2005]:

• Unit test• Regression test• Integration test• System test

A usability test was also performed on a low fidelity-prototype (LoFi) during the development ofthe system. The mentioned tests are subsequently explained.

9.1.1 Unit test

The unit test tests a single function or a group of functions and is used in order to verify if thefunction is working correctly. This is done continuously throughout the system development.Functions usually contain several internal variables, and the execution of the unit test is to test ifthe values of these internal variables are correct. This is done by writing the value of the variablein question to the console window. Another way is to debug. The debug mode makes it possibleto put breakpoints in the code execution and read the different variables that are processed step bystep. This test is done continuously throughout the system development and will therefore not befurther documented in the report.

9.1.2 Regression test

The regression test is a method, which is used to handle errors that appear when functions arechanged and inflicts errors on other functions in the system. This test is an integrated part of theiterative process of system development and will therefore not be further documented in the report.

9.1.3 Integration test

The integration test is done by testing larger parts of the system together. It is investigated if thedifferent components can work together as it was intended. The integration test is usually carriedout as a black box test, where the focus is on the input and output of the system, rather than theexecution of the internal commands.

43


Aim

The aim of the integration test is to find errors in the functionality and thereby investigating if theinput gives the expected result.

Method

A systematic test of all possible scenarios will be executed. This includes a test of the main flowof the system and possible alternative flows. An integration test will accentuate problems that canoccur by regular use of the system. In the scenarios triggering and stimulation a healthy subjectis used. As with the other test types used in the development phase, this test will be performedcontinuously while developing the system. Thus all software errors are corrected.

The scenarios are following the flow of the system with the following order:

• Login• Entering training/testing mode• Training/testing mode: Data acquisition• Training/testing mode: Data saving• Detector menu: INP template extraction• Detector menu: Set thresholds and build detector• Testing mode: Stimulation• Testing mode: Subject feedback

LoginAim:It should be tested if it is possible to login a subject and subsequently activatethe Training and the Testing button.Method:The user should type the name of the subject in the text field of the login screenand then presses OKInput:a) A name is entered in the text field and the user subsequently presses OK.b) A name is not entered and the user presses OK.Expected output:a) Training and Testing button are activated.b) An error message will appear, warning the user that a subject name isneeded.Result:a) The subject is logged in.b) A warning message appears when no name is entered.

Table 9.1: Result from the integration test of the scenario ”Login”.

44 9. Testing


Entering training/testing mode.Aim:It should be tested if it is possible to achieve the name from the login screenand get the current training/testing session number while possible previousrecorded sessions are considered.Method:After the user has entered the subject name in the login screen, the buttonTraining is pressed. In the training/ testing window it has to be controlled ifthe entered subject name is displayed and if the session number is correct.Input:a) An already existing subject name is entered in the text field and the userpresses OK and subsequently the Training button.b) A new subject name is entered in the text field and the user presses OK andsubsequently the Training button.Expected output:a1) The correct name and session number are displayed.a2) An incorrect name and session number are displayed.b1) The correct name and session number are displayed.b2) An incorrect name and session number are displayed.Result:The name and session number are displayed as described in case a1) and b1).

Table 9.2: Result from the integration test of the scenario ”Entering training/testing mode”.

Training/testing mode: Data acquisition.Aim:It should be tested if it is possible to achieve online EEG and EMG data intraining/testing mode and display these in the designated graphs.Method:The user presses Start. This should initiate the data acquisition for the graphs.Input:a) EEG and EMG electrodes are connected correctly to the system.b) EEG and EMG electrodes are incorrectly connected to the system.Expected output:a) Both EEG and EMG signals are displayed correctly.b) EEG and/or EMG signals are not present.c) EEG and/or EMG signals are displaying noise.Result:Both EEG and EMG signals are functioning as expected.

Table 9.3: Result from the integration test of the scenario ”Training/testing mode: Data acquisi-tion”.

9. Testing 45


Training/testing mode: Data savingAim:It should be tested if it is possible to save the recorded EMG and EEG datain the training/testing mode with the corresponding name and session number,when the user presses Save.Method:After starting a recording the user presses Stop. A dialog window will pop upprompting the user to decide whether the recorded data should be saved or not.Input:a) EEG and EMG signals, subject name and current training/testing sessionnumber.Expected output:The data from both EEG and EMG signals are saved correctly in a file, whichcarries the name of the subject, the training type and the session number.Result:The saving function is working as expected.

Table 9.4: Result from the integration test of the scenario ”Training/testing mode: Data saving”.

Detector menu: INP template extraction.Aim:It should be tested if it is possible to extract INP template from one or multipletraining sessions.Method:The user can access the detector builder in two ways. He can select to recordtraining sessions first and build the detector afterwards, or he can go to thedetector building directly from the login screen by pressing Testing. When theuser accesses the detector builder menu he can select several training sessionsby holding in the ALT-key on his keyboard. When the wanted training sessionsare selected the user has to enter the used gain and press Build detector menu.Input:One or multiple training sessions.Expected output:Three graphs are displayed in the detector menu, showing average INP, INPtemplate and standard deviation and power on the EOG channel for eachepoch.Result:INP template is extracted and the three graphs are displayed.

Table 9.5: Result from the integration test of the scenario ”Detector menu: INP template extrac-tion from multiple files”.

46 9. Testing


Detector menu: Set thresholds and build detector.Aim:It should be tested if it is possible to enter a threshold value for the power inthe EOG channel (FP1) and a threshold of true positive (TP) and false positive(FP) values based on a ROC-curve.Method:The user is entering a threshold value and is directed to the next window whichcontains a ROC-curve. Here the user selects a data point based on the ROCcurve, representing a false positive and true positive value, and enter this valuein the corresponding fields. If the user pushes the OK button, the system willprompt that the detector is build.Input:a) The user will enter a threshold value for FP1 and a valid data point of TPand FP values.b) The user will enter a threshold value for FP1 and an invalid data point of TPand FP values.Expected output:a) The detector is build if the input values are within the allowed limit.b) If the input values are out of bounce, an error message will be prompted.Result:The detector is build successfully when valid values are entered. If invalidnumbers are entered, the system will prompt an error message and the user hasto enter new value.

Table 9.6: Result from the integration test of the scenario ”Detector menu: Set thresholds andbuild detector”.

Testing: Stimulation.Aim:It should be tested if it is possible to send a trigger, based on a detected INPand toggle the stimulation mode on and off.Method:The subject will have the EEG cap mounted on the head and is instructed toimagine a movement. EEG signals are recorded in the testing mode. To iden-tify if a trigger command is send, the electrical stimulator is connected to anoscilloscope. The same setup is used when the radio button for the stimulationmode is enabled and disabled.Input:The EEG signal of the subject.Expected output:a) Stimulation enabled: a trigger command is send when an INP is detected,the stimulation signal can be seen on the oscilloscope.b) Stimulation disabled: a trigger command is send when an INP is detected,no stimulation signal can be seen at on the oscilloscope.Result:The stimulation was given, visualized on the oscilloscope, when an INP wasdetected with the stimulation mode enabled.

Table 9.7: Result from the integration test of the scenario ”Testing Mode: Stimulation”.

9. Testing 47


Testing: Subject feedback.Aim:It should be tested if it is possible to detect finger movements from the subjectto indicate when a stimulation has happened.Method:EMG signals are recorded from the index finger of the left and the right hand.a) The subject should extend his right finger, when he was thinking of a move-ment and got a stimulation, which will make the true positive (TP) count oneup.b) The subject should move his left index finger, when he was thinking of amovement, but got no stimulation, which will count false negative (FN) oneup.c) The subject should not move any of his fingers, when he was not thinkingof a movement, but got a stimulation, which will count false positive (FP) oneup.Input:EMG signal from the right and left index finger.Expected output:The counters from TP, FP and FN will be controlled from the two fingers re-spectively.Result:It was possible to control all counters with the two fingers.

Table 9.8: Result from the integration test of the scenario ”Testing: Triggering”.

9.1.4 System test

The system test verifies if the requirements stated in chapter 6 are fulfilled by the system. The testwill be done on the prototype of the system and is tested as a black box test, where only input tothe system is known.

Aim

The aim for the system test is to document that the functionality of the program fulfills the require-ments.

Method

The system test evaluates every single entry one by one. If any mismatches are found betweenthe requirements and the system under development, it has to be corrected. This documentation isfrom the last iteration of the system and will therefore fulfill all requirements.

48 9. Testing


Results

Requirements ResultsThe system will record nine channel EEGmeasured from the scalp.

It is possible to record from all EEG channelsconnected to the system, so the requirement isfulfilled.

The system will record one channel EOGmeasured from the eye.

It is possible to record from the EOG channelconnected to the system, so the requirement isfulfilled.

The system will record three channel EMGmeasured from the tibialis anterior muscleand the right and left index finger.

It is possible to record from all the EMGchannels connected to the system, so the re-quirement is fulfilled.

The system will allow the user to makerecordings with different movement parame-ters.

This requirement is fulfilled with the twodropdown menus in the training window,where the user can select between imaginaryand real movements and asynchronous andsynchronous movements.

The system will store all recordings locally. All data is saved on the computer which runthe system, so this requirement is fulfilled.

The system will display EEG and EMG sig-nals online.

This requirement is fulfilled. It is possible toshow all EEG signals by selecting the chan-nel of interest in the dropdown menu and itis possible to show the EMG from the tibialisanterior muscle, which is of interest to visu-alize during the experiment, unlike the EMGsfrom the index fingers.

The user will have the possibility to displayone of nine recorded EEG channels at a time.

As stated in one of the previous requirements,this demand is fulfilled.

The system will give visual feedback to thesubject about movement performance.

The subject will get a subject window dis-played which contains a bar, showing a liveimage of the movement performance, and acounter, thus this requirement is fulfilled.

The system will send a trigger signal when aINP is detected in order to stimulate.

An electrical stimulation is sent when a INPis detected, so this requirement are fulfilled.

Table 9.9: Results from the system test of the system.

9.1.5 LoFi usability test

The following test was performed in the initial phase of the system on a paper-based LoFi proto-type. The results from this test were discussed within the group and solutions were made, whereit was seen necessary.

Aim

The intention of this usability test is to see if the test user is able to move logically in and betweenthe ”Training” and the ”Testing” mode. Furthermore, it would be investigated if the placement ofthe different setting buttons can be improved in order to give the user a better overview. Finallythe test should help to detect redundant or missing functions.

9. Testing 49


Experimental procedure

The following usability test will include 4 test users in the age of 22 to 26. The test is performed ina usability laboratory at Aalborg University. During the performance, the test will be videotapedtogether with sound. The test user will sit in front of a table where a printed version of the LoFiprototype is presented. A task list will be given to the test user. One group member will sit nextto the test subject reading out the introductions F.1 and guide the test user through the test course.Whenever necessary it is allowed to help the test user. Another group member is taking notes.The test user is asked to filled out a questionnaire to evaluate the program. The answers of theanonymous questionnaires can be found in the appendix F.1.3. A summary of the answers is givenin the result part.

The task list:

1. Start the system using the short cut on the desktop.2. Choose the mode Testing for a subject named ”Hans Jenson”.3. Before starting the recording make the following settings:

• Change the EEG channel to e.g. Cz.• Choose Sync as BCI movement.

4. Make a recording in the length of 30 seconds.5. During the recording use the zoom function for both the graphs EMG and EEG.6. Make two more recordings and save only one of them.7. Press Build detector in order to look at the performance of the current algorithm.8. Change to mode Testing and make the following setting:

• Change the visibility settings for the subject window to Time disabled.9. Save the recording.

10. Leave the program.

Results

The test is evaluated by analysis of the video tapes and the questionnaire evaluation, where eachtask was evaluated with a score between 0-10 where 10 represented the best possible score and 0the worst, and a written comment. The following graph (figure 9.1) shows the average score ofeach question, where the average test score was 7.6.

Figure 9.1: Average score for each question of the questionnaire. The red line indicates the meanscore of the test users for all questions

50 9. Testing

9.2 Test of the finished system

Following is a summary over the key points said during the test and written in the questionnaires:

The login window is clear and easy to understand. Regarding to the changeable options, for ex-ample the channels and different training patterns, it is confusing for the subject to decide whenit is appropriate to change the options. Most of the options are understandable except the Builddetector button. Hence, it was suggested by the test subjects that the explanations of the some ofthe options should be more detailed. The interface was in general rated user friendly even thoughthe whole system seemed to become more clear and familiar after using it several times. The sav-ing option is simple and secure, but for the proficient user, it may be annoying. The structure ofwindow is reasonable, however, it would be better if the EEG and EMG display windows can beadjusted to a bigger size.

Based on the usability test, it can be concluded that the LoFi imitation of system basically is feasi-ble and functional and can be used of rehabilitation of stroke patients. The dominant problems arethe size of graphs and position of buttons. These problems are resolved during the implementationiterations.


The testing of the finished system is just as important as testing of the developing system. Thefinished system has to be tested with different goals in mind, compared to the tests performed inthe development of the system. While the test of the developing system is focusing on debugging,the following tests will investigate how the finished system is performing as a scientific tool andhow it is controlled.

The following tests are performed on the finished system:

• Performance test• Usability test

There will not be made any attempts to improve the system based on the test results.

9.2.1 Performance test

To investigate the performance of the system, a performance test has to be done. The performancetest measures the systems capability of detecting INP and give the trigger for the external device.The full experiment protocol can be found in appendix E.

Subjects

Four healthy subjects participated in the experiments. All subjects were from the work group andgave their informed consent before participating and were allowed to withdraw from the exper-iment at any time. None of the subjects had any known psychological disorders, sensory-motordeficits or neurologic disorders.

9. Testing 51


Experimental procedure

When the subject was equipped with the equipment, the subject was instructed about the experi-ment procedure. In order to get familiar with the setup the subject was asked to practice make aballistic dorsal finger flexion and a forceful ballistic dorsal foot flexion in the range of 20-30 % ofmaximum voluntary contraction.The experiment was divided into two parts. First, asynchronous real movements were recorded inthe training mode. After a detection algorithm was build from the training sessions, it was testedon imaginary asynchronous movements in the testing mode.In the training mode a dorsal flexion had to be done at random intervals. The subject was asked tofocus on the task just prior the movement. The dorsal flexion had to be quick and powerful and thesubjects were told to maintain the same force and rate during the session. In order to do so visualfeedback was provided to the subjects on a computer screen. Two training sessions were recordedwith a duration of 5 minutes respectivelyFor the asynchronous imagined movements in the testing mode, the subjects were asked to imag-ine the perception of doing a dorsal flexion.Due to the prematurely state of the system, the stimulation was not given to the subject, but to anoscilloscope, which displayed the stimulation.The subject should indicate with his right and left index finger whether the stimulation was initi-ated by a subject generated INP. These values for TP, TN and FP were counted automatically bythe system and the performance of the online stimulation was read directly from these results. Thesubjects were allowed to do real movements five seconds after having thought of a movement tohelp them imagining the movement. This phase continued until 50 TP values had been reached.

Results

Recordings from two subjects were excluded, due to an amplifier overload during the test. Thisresulted in the signal to get saturated, which means that no appropriate template could be derivedfrom the training session. Due to time constraints no new recordings were made. The performancetest of the online stimulation gave the following results, based on the two training sessions thatsucceeded:

Subject DetectionTPR %

FP/min Duration of testingsession

Total number oftasks attempted

1 66.67 1 32 752 65.78 1.3 27 76

Table 9.10: Performance test of the online detection of INP.

9.2.2 Usability test

Unlike the LoFi usability test, this test has the purpose to investigate how well a potential end-userinteract with the system. The following test was performed with the final version of the system.

Aim

The purpose of the usability test is get in-depth opinions from an experience group and to deter-mine whether the user is able to use the GUI for the rehabilitation of stroke patients.

52 9. Testing

9.3 Test summary

Experimental procedure.

The test users selected for this usability test, were users who already were familiar with similarsystems for recording EEGs. Due to their preknowledge from similar systems, the users couldgive expert comments during the test. This test was performed in an informal manner in the grouproom. A script located in appendix F.1, was read out to the test user in order to give an understand-ing of the background of the program. The test was carried out by giving an experienced user atask list, which the user has to perform.

The task list:

1. Start the system using the short cut on the desktop.2. Choose the mode Testing for a subject named ”Hans Jenson”.3. Before starting the recording make the following settings:

• Change the EEG channel to e.g. Cz.• Choose Sync as BCI movement.

4. Make a recording in the length of 30 seconds.5. Make two more recordings and save only one of them.6. Press Build detector in order to look at the performance of the current algorithm.7. Change to mode Testing and make the following setting:

• Change the visibility settings for the subject window to Time disabled.8. Save the recording.9. Leave the program.

The intention with this test was to discuss problems and get comments on the flow and appearanceon the system. While the test user was performing the mentioned tasks, one group member wasresponsible for making notes about any particular events or comments from the test user. Thesenotes are in appendix F.3. The key points are included in the result section.

Results

The expert group were in general very content with the system, although there were both minorand major things they mentioned should be replaced or added. The key points are explained below.A majority of the expert group stated that the EMG and EEG window should be labeled accordingto which signal they displayed. A majority also mentioned that a help guide added in the system,will help clarify questions, which the user encounters. Two users claimed that the system openstoo many windows, which will be confusing to operate with. Further comments were concerningthe layout and buttons where alternatives were suggested.

9.3 Test summary

The system was tested in several ways both during the development of the system and when thesystem was finalized. Continuously during the implementation of the system, there has been madeunit test and integration test so bugs could be mended as they appeared, thus no documentation isavailable for these tests. The integration test was done after the basic implementation to validateif consecutive parts could fit together. The integration test resulted in a positive result, since allexpected outcomes were fulfilled. The system test confirmed that the requirements were respected.The usability test in the development of the test was performed on non-experts and showed thatthe system is understandable, especially when the user gets acquainted with the system. In a scale

9. Testing 53

9.3 Test summary

between 0 to 10, the system under development got a score on 7.6. The test users came withseveral suggestions for improvements of the system, which is implemented in later versions of thesystem.Where the testing of the system in development concentrated on how the system works, the testsof the finished system focused more on how good the system works.The performance test measured the systems capability of detecting INP and give the stimulation,based on the results from the training session. Based on two subjects, the average true positiverate were 66.23 %.The second usability test was done on a working finished system with experienced users, andshowed that the users were satisfied with the system. Although the users had some minor andmajor changes as suggestion.

54 9. Testing

PART

IIISYNTHESIS

55

56

Chapter 10

DiscussionThe problem analysis contains several considerations, all related to the rehabilitation of stroke.The most important considerations are discussed in the first section of this chapter. Afterwards thedeveloped BCI system is discussed.

Current rehabilitation strategies like physiotherapy and robotic therapy share two characteristics.On one hand they are passive methods, meaning that the subjects initiation of movement cannot berespected. On the other hand they are methods based on repetitive movements with a constrainedmovement orientation. Unlike these a BCI system used for rehabilitation purposes has a differentapproach, since brain signals can be detected in order to provide feedback to the subject.The developed BCI system, this report refers to, can detect INP and send a trigger. In the imple-mented system the trigger is used in form of visual feedback. Furthermore, the trigger can be usedby the patient to control a variety of external devices (e.g. FES or robotic systems), which cansupport the movement execution.While treatments like CIMT expect that the patient has already a certain ability to move the im-paired limb, BCI systems only expect a certain level of cognitive awareness in order to adjust thesystem to the subject.Our BCI system uses the INP of MRCP due to the fact, that INP occurs before the onset of themovement, and thereby can be used to predict a movement. Other indicators, which can be usedas control signals for BCI, could be µ and β rhythms and P300 waves, all found in an EEG.MRCP was chosen as a control signal, due to previous research at Center for Sensory-Motor Inter-action (SMI) at Aalborg University. Unlike the current system, the previous system developed atSMI, worked with an offline detection of INP, where the system in hand is working with an onlinedetection of INP.

We managed to build a working system which is capable of detecting INP with an average TPR of66.23 %. However to obtain a statistical significant result on the performance of the system moresubjects are needed. It can be expected that the result would get better when more training trialsare included. Only results from 50 % of our subjects were acceptable for further analysis, due toproblems with the used amplifiers. These problems can be caused either by complications withthe equipment or the setup, e.g. high impedance. A solution for these problems could be to revisethe experimental protocol and use more reliable equipment.The used detection algorithm also have an influence on the performance, but it was not in the scopeof this project to improve this part of the system.The test results from the offline version of the system had a TPR of 63.3 %. With the online resultof 66.23 % it can be assumed that the two systems are performing similar. It can be argued thatthe performance test should be tested on rehabilitation subjects, due to the difference in the mor-phology of MRCPs compared to healthy subjects. Since the system is in the development phaseonly a proof of concept was required and therefore no tests were performed on stroke patients.Two usability tests were performed on the system. The first usability test was done on a LoFiprototype in the initial phase of the system development, and served as a guide for how the systemshould be designed. The LoFi prototype obtained a score of 7.4. This test was performed on reg-ular users in order to test the logic flow of the system.A second usability test was performed on the finished system in order to test the usability and im-prove the GUI. However, due to its high speciality the test was only carried out with users havinga background knowledge regarding BCI and EEG signals. The comments implied that the system

57

was user friendly and easy to navigate through, but had some flaws. The current system is focusingon research purposes. When this system should be used for rehabilitation in for instance clinics,another usability test has to be performed on the end-user segment before the system is released.

58 10. Discussion

Chapter 11

ConclusionIn Denmark it is estimated that there are 12.500 incidents of stroke every year of which 50 %are in need of rehabilitation. The post stroke rehabilitation is a very time consuming process andexpensive for the healthcare system.The current rehabilitation methods do not provide significant improvements concerning recoveryof motor dysfunction. A new approach might be an introduction of a BCI system for rehabilitationpurposes based on MRCP.

The goal for this project was to develop an online BCI system that can detect INP. The integrationtest showed that the system was capable of detecting INP and provide a trigger, while the systemtest proved that all the functional requirements were fulfilled. The performance test showed a TPRof 66.23 % for the online stimulation. However, the results were only based on two healthy sub-jects and therefore no statistical evidence can be concluded. In order to obtain a more valid resulta higher number of subjects is required.The usability test performed with experienced users supported the positive feedback from the LoFiusability test, but also revealed some flaws. For instance, an implemented help function as well asa FAQ would be of great benefit for the usability. Furthermore, the number of windows, presentedto the user, should be reduced in order to give a better overview and enhance the usability.As the system evolves and more features will be implemented, a code optimizing has to be doneto keep the feedback latency below 300 ms, which is necessary to induce neural plasticity.

Like most studies concerning BCI for therapeutic purposes, the tests for this system were carriedout only with healthy subjects. Therefore the obtained results cannot be generalized for patientswho are actually in need of BCI. Prior the prospective use of the system for rehabilitation, it hasto be tested on patients suffering from cerebrovascular disease.

59

60 11. Conclusion

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BIBLIOGRAPHY 67

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68 BIBLIOGRAPHY

PART

IVAPPENDICES

69

70

Appendix A

Hebbian principleD. O. Hebb postulated in 1949 a hypothesis about, that the timing of neural events are crucialparameters for the induction of changes in synaptic excitability. He stated the following:

”When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes partin firing it, some growth process or metabolic change takes place in one or both cells such that A’sefficiency, as one of the cells firing B, is increased.” [Hebb, 1949]

It originally postulated that the strength of a synapse between a pre-synaptic and a post-synapticneuron would increase when both neurons were activated in near-synchronicity. This model haslater been refined by in vitro and in vivo animal experiments where it showed that synaptic efficacyinduced by associative activity is dependent on the order of activity from the pre- and postsynap-tic neuron. If the postsynaptic neuron generated an action potential after the afferent stimulus hadgenerated an excitatory postsynaptic potential (EPSP) a long-term potentiation (LTP) was induced.When the postsynaptic cell is depolarized synchronously with the afferent input from another cellor two independent afferent signals reaches the same cell in sync, the LTP is called associative orHebbian. In contrast if the postsynaptic neuron spiked before EPSP was induced by afferent stim-ulus, it would result in a long-term depression. This principle is called the temporally asymmetricHebbian rule. [Classen et al., 2004], [Paulsen and Sejnowski, 2000].

The Hebbian principle has gained a lot of attention in the rehabilitation field. LTP is consideredto be an important cellular mechanism in learning and memory and by inducing changes whichwill strengthen the synaptic excitability, it would be possible to alter the plasticity of e.g. themotor cortex. A study by U. Ziemann which followed the PAS protocol proved the hypothesisthat motor learning prevents subsequent LTP-like plasticity but enhances subsequent LTD-likeplasticity. [Ziemann et al., 2004]

71

72 A. Hebbian principle

Appendix B

Transcranial magnetic stimulationTranscranial magnetic stimulation (TMS) can be used to investigate noninvasively and painlesslyperipheral nerves, nervous propagation along the corticospinal tract and spinal roots of the hu-man. The technique is based on the principle of electromagnetic induction and is widely used tostimulate both peripheral nerves and brain tissue (figure B.1) in studies encompassing e.g. motorcontrol, movement disorders, stroke, and plasticity. It has proved to be a versatile technique andis now also being used in combination with electroencephalography (EEG), functional magneticresonance imaging (fMRI) and single unit recording. [Wassermann and Lisanby, 2001]

Figure B.1: In transcranial magnetic stimulation a coil is placed on the scalp. The experimentalsetup is here illustrated for therapy to treat depression and schizophren.

The disadvantages of TMS is that it is limited to brain areas at or near the cortical surface, becausethe effect of the stimulation reduces with distance from the coil [Wassermann and Lisanby, 2001].The potential of applying TMS to neurorehabilitation is high because the method is routinelycombined with other techniques. [Rossini and Rossi, 2007], [Ziemann, 2011]

73

74 B. Transcranial magnetic stimulation

Appendix C

ElectroencephalographyElectroencephalography (EEG) is a medical imaging technique that reads scalp electrical activitygenerated by brain structures. It is a completely non-invasive procedure that can be applied re-peatedly to patients, normal adults, and children with virtually no risk or limitation. The typicallyclinical use of EEG can be to diagnose epilepsy, brain death, encephalopathy or monitor anesthe-sia. For research EEG is used in neuroscience, psychophysiological research and cognitive scienceand psychology. [Misulis and Head, 2003]

C.1 Generation of EEG rhythms

The electrical activity that can be recorded on the scalp is a summation of excitatory and inhibitorypostsynaptic potentials and action potentials. These potentials comes from neurons situated in themost superficial areas of the cerebral cortex, parallel to the scalp. The electrical signals comingfrom deeper regions, like basal nuclei have too small amplitude compared to the that generated bythe most superficial layers and thus they cannot be distinguished, but the activated thalamocorticalafferents interfere with the motor output. [Misulis and Head, 2003]

C.1.1 Cortical potentials

The neuronal task of transmitting information is mainly realized by synapses and action potentialsrevealed as electrical activity. [Misulis and Head, 2003]

49

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C. Classification of nerve fibers (in humans)

Despopoulos, Color Atlas of Physiology © 2003 ThiemeAll rights reserved. Usage subject to terms and conditions of license.

Figure C.1: The ionic circuit in depolarization. [Despopoulos and Silbernagl, 2003]

The receptor activation may have either an excitatory or inhibitory effect on the membrane site,thus excitatory or inhibitory postsynaptic potentials, depending on what ions they increase the

75

C.1 Generation of EEG rhythms

permeability to.These potentials are the most representative electrical activity that can be recordedfrom the scalp. The action potential consists of a transient depolarization of the dendritical mem-brane which is conducted along the soma’s and axon’s membrane by changes in its ionic perme-ability. Certain neurotransmitters act on the channels within the neuronal membrane in specializedsites and increase their permeability to the positive ions that are placed outside the cell in equi-librium state. This phenomenon is called depolarization (figure C.1). It creates in fact a dipole, anegative extracellular environment and a positive one inside the cell, that is actually the oppositeof the equilibrium state and moves progressively farther. When all of the channels within oneneuron’s membrane are open, a peak potential is reached, followed by repolarisation, which con-sists of the ionic channels shutting, which are time-dependent, and the returning to the equilibriummembrane potential. A refractory period follows, blocking possible bidirectional conduction. Themyelinated axons drive the influx faster that unmyelinated ones, due to the myelin sheath whichimplies propagation from node to node, preventing the ionic leak. [Misulis and Head, 2003]

C.1.2 Scalp potentials

The electrical activity recorded at the scalp surface is a summation of different electrical signalsthat cross the meninges, the skull and the scalp before being detected by scalp electrodes. In-side the brain most of the motor neurons have a vertical orientation with a superficial dendriticalramification, a large cell body perpendicular to scalp and deep axons. This creates a columnarorganization of the cerebral cortex. EEG is a recording of vector potentials generated by corticalactivity, with amplitude correlated with the total area of activated cortex and also with the synchro-nization of a number of activated neurons. When the most superficial cortical parts have a positivefield potential and the deeper ones are negative, the vector is vertically oriented and pointing tothe scalp electrodes with its positive ending. The scalp and the inactive tissues have an electricalactivity as well that is recorded by EEG. [Misulis and Head, 2003]

C.1.3 Classification of EEG rhythms

For obtaining basic brain patterns of individuals, subjects are instructed to close their eyes andrelax. Brain patterns form wave shapes that are commonly sinusoidal (figure C.2). [Misulis andHead, 2003]

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B. Recording the electroencephalogram (EEG)

C. EEG curves D. Stages of sleep, REM sleep

Despopoulos, Color Atlas of Physiology © 2003 ThiemeAll rights reserved. Usage subject to terms and conditions of license.

Figure C.2: The different types of brain waves; Alpha (8-13 Hz), Beta (14-30 Hz), Theta (4-7Hz) and Delta (0.5-3 Hz). [Despopoulos and Silbernagl, 2003]

Usually, they are measured from peak to peak and normally range from 0.5 to 100 µV in amplitude,

76 C. Electroencephalography

C.2 Recording EEG

which is about 100 times lower than ECG signals. By means of Fourier transform power spectrumfrom the raw EEG signal is derived. In power spectrum contribution of sine waves with differentfrequencies are visible. Although the spectrum is continuous, ranging from 0 Hz up to one half ofsampling frequency, the brain state of the individual may make certain frequencies more dominant.[Misulis and Head, 2003]

C.2 Recording EEG

Surface electrodes are the most common method for recording EEG. Each of the electrodes arefixed to the skin and consist gel, which give a better connection between the electrode and the skin.There are many other ways of recording EEG, such as using needle, sphenoidal, subdural strip ordepth electrodes.

C.2.1 Electrode position

The electrodes are strategically placed across the scalp and a reference electrode is usually placedon one or both earlobes. The electrodes are placed according to The International Federation 10-20system (figure C.3). The letters F, T, C, P and O stands for Frontal, Temporal, Central, Parietal andOccipital. Even numbers refers to electrode positions on the right hemisphere and odd numbersrefer to positions on the left. The skull is divided into the transversal and median planes with 10% and 20 % intervals between each electrode. [Misulis and Head, 2003]

Figure C.3: The 10-20 electrode placement system for recording EEG signals. A) Superior view,B) left side view.

C.2.2 Artifacts

Among basic evaluation of the EEG traces belongs scanning for signal distortions called artifacts.Usually it is a sequence with higher amplitude and different shape in comparison to signal se-quences that doesn’t suffer by any large contamination. The artifact in the recorded EEG maybe either patient-related or environmental. Biological artifacts are unwanted physiological signalsthat may significantly disturb the EEG. Environmental artifacts, such as AC power line noise, canbe decreased by decreasing electrode impedance and by shorter electrode wires. [Misulis andHead, 2003]

C. Electroencephalography 77

C.2 Recording EEG

The most common EEG artifact sources can be classified in following way:

Biological artifacts:

• Eye-induced artifacts (EOG) which causes sharp spikes in the EEG when blinking or doingeye movements.

• Cardiac artifacts (ECG) where signals from the heart are picked up in the EEG.• Muscle activation (EMG) artifacts.• Heavy respiration gives slow and rhythmic artifact.• Sweating gives bad contact between the electrode and the skin.

Environmental artifacts:

• Alternating current artifact (50/60 Hz noise).• Cable movements.• Electronic devices (cell phones, wireless devices, radio etc.).

To remove the environmental and biological artifacts the signals acquired from the brain are am-plified and filtered, using both analogue and digital filters. Normally a digital low-pass and notchfilters are used.

78 C. Electroencephalography

Appendix D

Movement-related cortical potentialsMovement-related cortical potentials, also known as MRCP, represent averaged electroencephalo-graphic activity in the motor cortex related to a voluntary movement. [Yom-Tov and Inbar, 2003]

D.1 Origin/measuring of MRCP

MRCP can be measured from EEG recordings of the motor cortex in the brain. The EEG-signalis produced by firing of the neurons within the brain, here especially in the motor cortex, whichproduces an electrical activity that is recorded from the scalp. It is not possible to detect theelectrical potential of one single firing neuron due to the low amplitude of the discharge of theneuron, therefore the EEG that get picked up on the scalp is a summation of thousands synchronousfiring neurons. Here it also should be taken into account that voltage fields fall off with the squareof the distance, so it is hard to detect EEG-signals from the center of the brain. [Misulis and Head,2003]

D.2 History of MRCP

First experiments for recording movement-related activities were done by in 1951 by using pho-tographic superimposition of multiple single-sweep EEG traces. This approach was kind of un-successful cause only post movement potential could be detected due to the low signal-to-noiseratio. Kornhuber and Deecke discovered in 1964 and onwards the most significant components ofwhat we call MRCP today, Bereitschaftspotential (BP) or Readiness Potential (RP), pre-motionpositivity (PMP), motor potential (MP) and reafferent potential. Only latter mentioned componentwas localized after the movement onset. [Shibasaki and Hallett, 2006], [Kornhuber and Deecke,1964]

D.3 Composition of MRCP

There are a broad variety of names concerning the individual components of MRCP. In the fol-lowing we will mention the commonly used terms to describe the morphology of the signal. TheBereitschaftspotential (BP) can be differentiated in the phases early BP and late BP. The early BPalso called negative slope 1 (NS1) and BP1 represents the first slow increase in negativity. Thefollowing late BP, also known as NS2 and BP2, is a second steeper slope and can be distinguishedfrom the early BP due to its abrupt increase of gradient. While the late BP is considered as to bemore specific for the site of movement, the early BP demonstrates more the general preparationfor the forthcoming movement due to its spread out dispersion over the scalp. The maximum neg-ativity is called MP or peak BP. Finally, the positive shift of the signal refers to reafferent potential.[Jahanshahi and Hallett, 2003], [Shibasaki and Hallett, 2006]

D.4 Time course and correspondent activated areas in the brain

Okano and Tanji’s investigated the neuronal activity on monkeys in the supplementary motor area(SMA), premotor cortex and primary motor cortex (M1) prior for triggered and self-paced move-

79

D.5 Influences on the BP

ments. They concluded that there are two types of neuronal activities in these particulars fields ofthe brain, long lead activity and short lead activity. The former starts 1 to 2 s before the movementand predominant among SMA, were found prior to self-paced movements but less in triggeredmovements. However, short lead activity, occurring about 480 ms prior the movement onset,could be seen in the SMA, premotor cortex and M1 for both kinds of movement triggered andself-paced respectively. [Orkano and Tanji, 1987]

These discoveries lead to the following major hypotheses: The first hypothesis purposes a serialactivation of the SMA and M1 where the latter follows the SMA activation. The second hypothesispurposes a parallel activation of both SMA and M1. Additionally, SMA activation last until aroundthe onset of EMG activity. Nevertheless, the early (BP) starts about 1000 to 2000 ms before themovement onset (detected by EMG signal) in the supplementary area SMA including pre-SMAand SMA proper, followed by activation in the lateral premotor cortices bilaterally. The startingpoint of early BP varies dependent on the kind of movement and the subject. Hence, BP startsmuch earlier if the subject is repeating a certain movement in short periodic distances. In contrast,for movements which are accomplished under more natural conditions, early BP starts later. About400 to 500 ms prior voluntary movement late BP occurs in the M1 and premotor cortex. The MPoriginates in a small area of M1 referring closely to somatotopic organization about 50 to 60 msbefore EMG onset. Concerning PMP, the generator sources are not well known yet. [Neshigeet al., 1988], [Jahanshahi and Hallett, 2003], [Shibasaki and Hallett, 2006]

D.5 Influences on the BP

Both, magnitude as well as chronological sequence of BP recorded in the self-paced condition isdependent on divers factors, for example level on intention, preparatory state, movement selection(freely vs. fixed), learning and skill acquisition, pace of movement repetition, praxis movement,perceived effort, force exerted, speed and precision of movement, discreteness and complexity ofmovement, and pathological lesions of various brain structures.

Different influence factors on early and late BP:

Factors Early BP Late BPLevel of intention Larger Not clear distinguishedPreparatory state Earlier onset Not clear distinguishedMovement selection Larger No effectLearning Larger during learning Not clear distinguishedPraxis movement Start parietally Not clear distinguishedForce Larger Not clear distinguishedSpeed Later onset Not clear distinguishedPrecision No effect LargerDiscreteness No effect LargerComplexity No effect Larger

The factors itself cannot throughout be seen separately. Thus, exertion of stronger force usuallycome along with greater intention, motivation and effort of the movement. [Kitamura et al., 1993]investigated the complexity of finger movements and showed a larger amplitude of late BP forsimultaneous extension of two fingers which represents a more complex movement instead ofextension only one finger. However, another influence on the amplitude of BP has the selected

80 D. Movement-related cortical potentials

D.6 Interpretation of MRCP

movement. Freely selected movements showed a higher amplitude in opposition to fixed ones[Praamstra et al., 1995]. When considering the precision of a movement with focus on the forceproduction, a larger late BP could be seen for target force production in comparison to simpletask [Masaki et al., 1998]. In relation to the speed of a movement, BP starts later (means it iscloser to the movement onset) as faster a movement is carried out. [Taylor, 1978] investigated thedevelopment of BP amplitude when subjects repeatedly were pressing a given series of buttons asfast as possible. It could be seen that BP increased steadily with improvement of the learned skill.[Shibasaki and Hallett, 2006]

D.6 Interpretation of MRCP

MRCP or BP is a movement related potential, which means that it contains information about theonset of voluntary or imaginary movement. Due to the fact that BP precede 1.5 to 2 s before theonset of movement it was considered as a characteristic of motor preparation. [Libet et al., 1983]found out that cerebral launching of an spontaneous voluntary act starts unconsciously before any”decision” of acting is made leading him to the conclusion that BP represents an intention to act.Other suggested BP indicates resource mobilization [McCallum and Curry, 1993], effort [Wesselet al., 1994], [Kristeva et al., 1991], and timing of movements Deecke et al. [1985]. [Jahanshahiand Hallett, 2003]

D.7 Appearance of MRCP on a cellular level

It could be seen that the an extracellular increase of K+ concentration together with decrease ofCa++ concentration trigger areas in the brain and yield in negative slow potentials. Hence, as BPis a surface negative potential it is presumed that it indicates neuronal activation whereas a surfacepositive potentials such as P300 indicates neuronal inhibition. [Skinner and Yingling, 1976]Furthermore, slow negative cortical potentials like BP supposedly correspond to excitatory post-synaptic potentials (EPSP) [Caspers and Speckmann, 1974].

D. Movement-related cortical potentials 81

D.7 Appearance of MRCP on a cellular level

82 D. Movement-related cortical potentials

Appendix E

Experimental protocol for performance testingE.1 Purpose

To investigate the performance of the system, a performance test has to be done. The performancetest measures the systems capability of detecting INP and give the stimulation. This test result willlater be compared to the performance of an offline version of the system.

E.2 Equipment

E.2.1 Technical equipment

• EEG electrodes (AMBU, Blue Sensor M CHECK)• EMG electrodes (AMBU, Neuroline 721)• EEG wires• BNC cables• Wrist band• Isolated EMG amplifier EM006-01• National Instrument BNC2090 Data Acquisition card, 16/2 I/O, resolution @12 bits, sam-

pling rate 200kHz [Instruments, 2005]• Foot fixture• Ergonomic armchair• Computer with MATLAB version R2010b installed

E.2.2 Technical subject preparation

• Acetone• Peeling gel• Cotton swabs• Tape• Disposable razor• Measuring tape

E.3 Subjects

E.3.1 Inclusion criteria

• The subject should be healthy.• The subject have to give his/her written consent and understand the experiment.

E.3.2 Exclusion criteria

• The subject must not have any neurologic disorders, sensory-motor deficits or psychologicaldisorders.

• The subject must not be undergoing current treatment.

83

E.4 Experimental settings

• The subject must not be pregnant.• The subject must not be influenced by any kind of drugs.• The subject must not have any limbs amputated.

E.3.3 Informing the subject

The subject is informed about the proceedings of the experiment and is allowed to resign from theexperiment at any time. After a written consent is obtained from the subject.

E.4 Experimental settings

The experimental setup is divided into three parts; preparation of the subjects, hardware and soft-ware.

E.4.1 Preparation of the subjects

The room used for the experiment is electronically shielded from interference from foreign sources.The subject is placed in a comfortable armchair with it is dominant foot placed in an custom madefixture and a strong velcro band is used to hold the foot still. A force transducer is located at theend of the fixture. The upper leg makes a 90 degree angle with the lower leg.

Placement of EMG electrodes

First, tibialis anterior muscle is located by palpation while the subject is requested to make differ-ent movements, e.g. an ankle dorsiflexion. Then the area where the muscle is located has to beshaved and cleaned with acetone. After the EMG electrodes are placed on the muscle next to eachother with a gap on about 1 mm. The ground electrode was placed on fibula. This procedure isrepeated for the dorsal interosseus muscle on the left and the right hand with a ground electrodeon the wrist.

Placement of EEG electrodes

By the help of a measuring tape ten EEG electrodes are placed according to the international 10-20 system in the following positions: Cz, C3, C4, F3, Fz, F4, P3, Pz, P4, where the last is placedat FP1 measuring EOG. Each marked spot on the scalp is prepared with peeling gel before theelectrode is placed in order to reduce the signal impedance. All EEG electrodes are referenced tothe earlobe and have to obey an impedance level below 5 KΩ.

E.4.2 Hardware

The following devices have to be turned on:

• Computer• Biosignal amplifier (DC coupled, custom made at SMI Aalborg University)• DAQ-card

84 E. Experimental protocol for performance testing

E.5 Method

EEG and EOG signals are preamplified by a gain of 5000 and bandpass filtered from 0-100 Hz.EMG signals are preamplified by a gain of 1000 and bandpass filtered from 20-500 Hz beforereaching the DAQ-card.

E.4.3 Software

EMG and EEG signals were acquired from the DAQ-card at a sampling frequency of 500 Hz anddownsampled to 20 Hz. EEG signals were bandpass filtered with a forth order butterworth from0.05 to 10 Hz, while the EMG signals obtained from the index fingers were filtered with a secondorder Butterworth from 30-250 Hz.

E.5 Method

Before starting the experiment the subject is instructed about the following experiment procedure.In order to get familiar with the setup the subject was asked to practice make and ballistic dorsalfinger flexion and a forceful ballistic dorsal foot flexion in the range of 20-30 % of maximum vol-untary contraction. The experiment is divided into two parts. First, asynchronous real movementsare recorded in the training mode. After a detection algorithm is build from the sessions and istested on imaginary asynchronous movements in the testing mode.

E.5.1 Tasks

Asynchronous real movements

In the training mode a dorsal flexion had to be done at random intervals. The subject was askedto focus on the task just prior the movement. The dorsal flexion had to be quick and powerful andthe subjects were told to maintain the same force and rate during the session, helped by a visualfeedback from a computer screen.The synchronous real movement in training mode was done for5 minutes and repeated two times.

Asynchronous imagined movements

For the asynchronous imagined movements in the testing mode, the subjects were asked to imag-ine the perception of doing a dorsal flexion at random intervals. Due to the prematurely state of thesystem, the stimulation was not given to the subject, but to an oscilloscope, which displayed thestimulation. The subject should indicate with his right and left index finger whether the stimulationwas initiated by a subject generated INP. These values for true positive, true negative and false pos-itive were counted automatically by the system.The subjects were allowed to do real movementsfive seconds after having thought of a movement to help them imagining the movement. Thisphase should continue until 50 true positives has been registered.

E. Experimental protocol for performance testing 85

E.5 Method

86 E. Experimental protocol for performance testing

Appendix F

Usability testF.1 Test script

The following section is the script which is read out loud by the test performer to the test user inorder to give the user an understanding of the background of the program and which tasks the testuser needs to perform.

F.1.1 Instruction to the test user

First of all thank you very much for participating in this test. This is a usability test of a graphicaluser interface (GUI) which will be used for rehabilitation of stroke patients. The program is devel-oped by Group 855a from Biomedical Engineering and Informatics, 8th semester and Imran KhanNiazi. Stroke patients are often suffering from constricted ability to move peripheral parts of theirbody, e.g. the hand, due to the placement of the blood cloth in the motor cortex. The GUI is partof a Brain-Computer Interface (BCI) and allows the user to make recordings from EEG signalswhile the subject tries to perform a certain movement, which he/she is not able to execute in real-ity due to the outcome of stroke. However, the mental movement preparation induce certain brainsignals, which can be used to train a detection algorithm. This algorithm is used to detect onlinein a testing session. Every time a movement intention is detected, the subject gets an electricalstimulation at the corresponding. For the test scenario, please imagine that you are the user whowould like to make recordings by using the GUI.

During the test you are welcome to speak out loud what you are thinking when solving the task.You can describe how the interaction feels, mention any function that annoys you or tell us if youproblems solving the task.

F.1.2 Task list

The following task list is for the usability test for the expert group only.

1. Start the program using the short cut on the desktop.2. Choose ”Hans Jenson” as subject name (type: HansJenson)3. Choose the mode Training.4. Before starting a recording make the following settings:

• Change the EEG channel to e.g. Cz.• Choose Sync at testing pattern.

5. Start data acquisition by pressing ’Start’.6. Make a recording of 10 seconds and save it.7. Press Detector button.8. Select two sessions from the subject ”Hans Jenson”.9. Choose a gain of 1000 and press Preprocessing.

10. Choose a threshold of 0.1 and build the detector.11. Choose one data pair from the ROC curve (x-axis = TP, y-axis = FP).

87

F.2 Answers from questionnaire

12. Before starting a recording in the mode Testing make the following settings:• Turn off the electrical stimulation.• Choose Async as testing pattern.• Change the visibility settings for the subject window to Time disabled.

13. Start data acquisition by pressing Start.14. Press Calibrate about 3 seconds later.15. Make a recording of 10 seconds and save it.16. Leave the program.

During the usability test, one group member is responsible for making notes about any particularevents or comments from the test user.

F.1.3 Questionnaire

After performing the usability test the user is asked to evaluate the program. The user is askedto answer questions in written form and marking each questions with points from 0 to 10 pointswhere 10 is the best possible way and 0 is the worst possible way to solve the problem.

Clarity of the windows (e.g. placement of the buttons, problems in finding a certain function inorder to solve a task, button size...):

1. Please evaluate the clarity of the login screen.2. Please evaluate the clarity of the training window.3. Please evaluate the clarity of the detector window.4. Please evaluate the clarity of the testing window.5. Please evaluate the clarity in general.

Functionality:

1. How far is the program flow intuitive.2. Which part of the program has to be improved and decribe the reason.3. Please evaluate the functionality in general.


The following are the answers to the questionaries for the usability test on the LoFi test.

F.2.1 Questionnaire from person 1

Give the questions points from 0 to 10 points where 10 is the best possible solution and 0 is theworst possible solution to solve the problem and give a short written answer.

1) Did you know what to do, when you saw the welcome screen (window where you had toenter the subject name)?9: Yeah.. it was pretty obvious that u should enter the name of the subject to save it as a file namefor later.

88 F. Usability test


2) How good do you think worked the different options (e.g. change of the EEG channel) inthe training and testing interfaces?7: It was good, but maybe a little hard to understand how to do, and when.

3) Was there an option you did not understand?6: When pressing the record button, it was a little unclear whether you had to press start or record.

4) Was the testing and training interface self-explanatory?8: Yeah... Pretty much. Not too hard to understand and not to easy. It had what it required andthe interface was very user friendly.

5) What do you think about the saving option?8: Good.

6) What do you think about the location of the button and graph, are they comfortable andreasonable?7: Yeah, they were comfortable. maybe the buttons should have been in the left side, because weare used to bottons in the left side.

7) Any thing else to add?8: No... it was a good product, and easy to understand. Not too much too say.



1) Did you know what to do, when you saw the welcome screen (window where you had toenter the subject name)?8: Yes it was clear.

2) How good do you think worked the different options (e.g. change of the EEG channel) inthe training and testing interfaces?7: It was okay, but I did not get that when I was choosing the specific channel it showed only thatchannel. I thought it showed a collected signal of e.g. 3 or 5 channels.. Otherwise it was easywith the start/stop buttons.

3) Was there an option you did not understand?Yes the shown channel of EEG like mentioned above.

4) Was the testing and training interface self-explanatory?8: Yes I think so. But maybe it does not have to be so self-explanatory because when the user haveused the program the first time he will know it in the future where he will be using it many times,compared to a user who will only use the program once and like really rare like the Dia-Finder test.

F. Usability test 89


5) What do you think about the saving option?7: It is okay, but in normal I would guess that the data is just saved when I press record, but maybeit is a good idea to ask the question if you want to save it if you have made a bad recording. Butalso if you are the experiment leader you are skilled with making measurements and you won’t thatmany errors, and here it would maybe be annoying that you need to answer this question everytime.

6) What do you think about the location of the button and graph, are they comfortable andreasonable?7: The graphs could be bigger but the buttons are fine.

7) Any thing else to add?No



1) Did you know what to do, when you saw the welcome screen (window where you had toenter the subject name)?8: It was a very simple screen with only 3 buttons and a text field

2) How good do you think the different options worked (e.g. change of the EEG channel) inthe training and testing interfaces?8: Easy

3) Was there an option you did not understand?6: A bit confusing with all the options and some of the options are written as abbreviations withoutany explaining

4) Was the testing and training interface self-explanatory?5: Was there any difference?

5) What do you think about the saving option?10: Simple and secure

6) What do you think about the location of the button and graph, are they comfortable andreasonable?8: The structure was okay. Maybe the start button and record button should be one button.

7) Any thing else to add?No


F.3 Usability test for expert group



1) Did you know what to do, when you saw the welcome screen (window where you had toenter the subject name)?8: It was pretty much a standard welcome so that was fine.

2) How good do you think worked the different options (e.g. change of the EEG channel) inthe training and testing interfaces?7: It was easy to change the channels. Zooming buttons confused me a little. Maybe put them onthe right side of the graphs. Or the buttons for the left graph on the left side and the buttons forthe right graph on the right side.

3) Was there an option you did not understand?9: Build classifier but it was never used. Most were pretty easy to understand.

4) Was the testing and training interface self self-explanatory?9: Yeah they were fine.

5) What do you think about the saving option?9: It made sense and was easy to use. Also when you had to not save the data.

6) What do you think about the location of the button and graph, are they comfortable andreasonable?7: It’s good to have the graphs that are important in the middle but it took a little time to get anoverview of the interface.

7) Any thing else to add?No not really.


The following is bullets are collected from the expert group usability test is not necessarily repre-sentative for the whole group and are in no specific order.

• In general a good and useful layout.• EMG and EEG graph windows carried no name for what they displayed.• The dropdown menu containing Async/sync and real/imaginary, should be radio buttons

instead, do to the few options to select from.• Instead of the current name for the detector button, use create template instead.• A help guide explaining how to operate the system and a FAQ could be very useful for this

system.• The system opens too many windows. It could be confusing for the user.• there should be only the countdown in the subject window (not the time in sum). Only

necessary information for the subject should be displayed.

F. Usability test 91


• A scaling option for the two windows would be very helpful• The user should be able to get feedback from how good subject performs the movement,

e.g. the bar window may be placed on the user window as well.


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VWHPIRUVWURNHUHKDELOLWDWLRQ - Forside - …vbn.aau.dk/files/52710516/MRCP_based_brain_computer... · 2011-06-01 · Title: MRCP-based brain-computer interface system for stroke rehabilitation