CHA

Neuper & Klimesch (Eds.)

Progress in Brain Research, Vol. 159

ISSN 0079-6123

Copyright r 2006 Elsevier B.V. All rights reserved

PTER 25

Motor imagery and EEG-based control of spellingdevices and neuroprostheses

Christa Neuper1,�, Gernot R. Muller-Putz2, Reinhold Scherer2 and Gert Pfurtscheller2

1Institute of Psychology, University of Graz, Universitatsplatz 2/III, A-8010 Graz, Austria2Laboratory of Brain–Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology,

Krenngasse 37, A-8010 Graz, Austria

Abstract: A brain–computer interface (BCI) transforms signals originating from the human brain intocommands that can control devices or applications. With this, a BCI provides a new non-muscular com-munication channel, which can be used to assist patients who have highly compromised motor functions.The Graz-BCI uses motor imagery and associated oscillatory EEG signals from the sensorimotor cortex fordevice control. As a result of research in the past 15 years, the classification of ERD/ERS patterns in singleEEG trials during motor execution and motor imagery forms the basis of this sensorimotor-rhythm con-trolled BCI. The major frequency bands of cortical oscillations considered here are the 8–13 and 15–30Hzbands. This chapter describes the basic methods used in Graz-BCI research and outlines possible clinicalapplications

Keywords: brain–computer interface (BCI); motor imagery; sensorimotor rhythm; event-relateddesynchronization (ERD); event-related synchronization (ERS); neuroprosthesis; virtual keyboard

Introduction

The relevance of the study of the brain’s oscilla-tory activity with respect to practical applicationshas been made evident in the area of brain–com-puter interface (BCI) research. The BCI technol-ogy allows the communication between people andmechanical devices and translates the human men-tal activity into device commands, for example, toassist patients who have highly compromised mo-tor functions. This has been made possible due toadvances in methods of EEG analysis and in in-formation technology, associated with a better un-derstanding of the functional significance ofcertain EEG parameters. By means of a BCI theongoing EEG signal is used to operate computer-

�Corresponding author. E-mail: christa.neuper@uni-graz.at

DOI: 10.1016/S0079-6123(06)59025-9 393

controlled devices. The kernel of this technology isan algorithm that takes samples, extracts features,and classifies the EEG signal in real time.

The so-called Graz-BCI, developed by Pfurtschel-ler’s group at the Graz University of Technology inthe early nineties, was the first online BCI systemusing ERD classification in single EEG trials to dis-criminate between different types of motor executionand motor imagery (Kalcher et al., 1996; Pfurtschelleret al. 1997; for a review see Neuper and Pfurtscheller,1999). Following the first basic studies, ERD dur-ing motor imagery has been investigated for its us-ability for device control (Pfurtscheller et al., 2000a,2003; Neuper et al., 2003; Scherer et al., 2004; Muller-Putz et al., 2005). The standard protocol of theGraz-BCI still serves as a reference paradigm forseveral groups (e.g., Cincotti et al., 2003; Wang et al.,2004; Coyle et al., 2005).

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In the first session of the standard protocol usershave to imagine different kinds of movement(e.g., hand, feet, or tongue movement) while theirEEG is being recorded. Based on this screeningsession signal features are extracted and a user-specific classifier is set up, which determines whichmovement the user is imagining. In subsequenttraining sessions the user receives feedback of mo-tor imagery related changes in the EEG. In par-ticular, mu and central beta rhythms, which arestrongly related to the functions of the motor cor-tex and the adjacent somatosensory cortex (re-viewed in e.g., McKay, 2005; Pineda, 2005), areused to determine the feedback.

The mu rhythm comprised a variety of different8–13Hz components distinguished from eachother by location, frequency, and its specific reac-tivity to sensory input or motor output(Pfurtscheller et al., 2000b). Often, these 8–13Hzoscillations are associated with 15–30Hz betarhythms, which are, however, clearly separablefrom the 10-Hz components in both topographyand timing (e.g., Salmelin and Hari, 1994;Pfurtscheller et al., 1998). A characteristic featuredefining the mu rhythm is that it attenuates in onecerebral hemisphere during preparation of contra-lateral extremity movement (Pfurtscheller andBerghold, 1989), the thought of a contralateralmovement (Pfurtscheller and Neuper, 1997), ortactile/electrical stimulation of a contralateral limb(Neuper and Pfurtscheller, 2001b; Muller et al.,2003b). Because these rhythms are associated withcortical areas most directly connected to thebrain’s normal motor output channels, they areparticularly promising for BCI research.

Classification of ERD/ERS patterns during motor

imagery

Most relevant for BCI use is the fact that no actualmovement is required to modulate the sensorimo-tor rhythms (SMRs) (Pfurtscheller and Neuper,1997). There is increasing evidence that character-istic, movement-related oscillatory patterns mayalso be linked to motor imagery, defined as mentalsimulation of a movement (Jeannerod and Frak,1999). It is generally accepted that imagining is

functionally equivalent to and may share some ofthe brain processes associated with real perceptionand action (Solodkin et al., 2004). By means ofquantification of ERD and ERS in time and space(Pfurtscheller and Lopes da Silva, 1999) it has beenshown that motor imagery can induce differenttypes of activation patterns, as for example: (i)desynchronization (ERD) of SMRs (mu rhythmand central beta oscillations) (Pfurtscheller andNeuper, 1997), (ii) synchronization (ERS) of themu rhythm (Neuper and Pfurtscheller, 2001a), and(iii) short-lasting synchronization (ERS) of centralbeta oscillations after termination of motor im-agery (Pfurtscheller et al., 2005c).

For the control of an external device based onbrain signals, it is essential that imagery-relatedbrain activity can be detected in real time from theongoing EEG. It has been documented that imag-ination of simple movements elicits predictablechanges in the sensorimotor mu and beta bands,which are very stable over time (i.e., small intra-subject variability; for a review see Neuper andPfurtscheller, 1999). But there is also disagreeingevidence of a portion of participants, who do notshow the expected imagination-related EEGchanges. Moreover, a diversity of time–frequencypatterns (i.e., high inter-subject variability), espe-cially with respect to the reactive frequency com-ponents, was found when studying the dynamics ofoscillatory activity during movement imagination(cf. Wang et al., 2004; Neuper et al., 2005;Pfurtscheller et al., 2005c).

Selection of relevant features (frequency, location)for BCI control

In a recent study, we addressed the importance ofoptimizing the BCI input features for each partic-ipant with respect to frequency and electrode lo-cation. For this purpose data of 34 healthysubjects, obtained during the first session of thestandard BCI protocol, were analyzed. The EEG(band pass 0.5–100Hz) was recorded from sixelectrodes placed over the cortical hand areas (atpositions C3 and C4 as well as positions 2.5 cmanterior and posterior to these; see Fig. 1A). Eachtrial started with the presentation of an acousticalwarning tone and a fixation cross. One second

Fig. 1. A: Relevant frequency components for the discrimination between left and right motor imagery when placing electrodes 2.5 cm

anterior (a) or posterior (p) to electrode position C3 and C4 (central, c). B: Distribution of electrode placements achieving the best

results. C: Best classification accuracies achieved with standard bands (10–12 and 16–24Hz) compared with subject-specific optimized

bands.

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later, an arrow (cue) pointing to the left (left hand)or to the right (right hand) specified the motorimagery task to perform. Each subject had to per-form the motor imagery for 4 s, until the screencontent was erased. After a short pause the nexttrial started. Each training run consisted of 40 tri-als with 20 trials per class presented in randomizedorder. Five training runs were recorded for eachsubject.

Band power features were computed by bandpass filtering the EEG signal, squaring and aver-aging the samples in the analyzed 1-s time window.From this averaged value the logarithm was cal-culated. For classification Fisher’s linear discrimi-nant analysis (LDA) was applied to the bandpower estimates (sample-by-sample). To identifythe most reactive frequency bands the sequentialfloating forward selection (SFFS) feature selection

algorithm (Pudil et al., 1994) was applied to thedata. Three independent analyses were performedon three different bipolar electrode combinations(same for both hemispheres): anterior–central(a–c), central–posterior (c–p), and anterior–poste-rior (a–p) (see Fig. 1A). These setups allow forrefining both electrode spacing (small vs. largedistance) and location (more anterior vs. pos-terior).

The trials were subdivided into N ¼ 17 overlap-ping time intervals of 1-s length and a time lag of0.5 s. For each interval 72 overlapping frequencycomponents between 6 and 30Hz with bandwidthsof 2, 4, 6, and 8Hz were calculated for each chan-nel. With the features obtained from each intervalindividual SFFS runs were computed. The taskwas to identify four features that best discriminatebetween the two brain patterns (left vs. right hand)

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within the 4-s motor imagery period. A 10� 10cross-validation procedure was applied to avoidover fitting and enhance the generalization of clas-sification results (Duda et al., 2001). Examples ofresults obtained from this extensive selection pro-cedure of relevant input features are summarizedin Fig. 1.

Figure 1A shows histograms of the identifiedfrequency components over all subjects, separatelyfor each hemisphere (electrode positions C3 andC4). For the majority of the participants (27 of 34)frequency components in the alpha band, espe-cially components above 10Hz, were selected. Incontrast to the clear peak in the alpha band, thefrequency distribution of relevant beta band com-ponents across subjects was more widespread, in-dicating higher variability between subjects. Thesedata confirm that for recognizing imagination ofmovement, upper alpha or mu components(11–13Hz) are the most relevant features of sen-sorimotor activity (Neuper and Pfurtscheller,2001a; Neuper et al., 2005; Pfurtscheller et al.,2006a). Higher significance of frequency compo-nents in the alpha as compared with the beta rangeduring imagination of finger movement was alsoreported earlier (Neuper and Pfurtscheller, 1999).Of interest is further, that the best electrode setup(see pie chart in Fig. 1B) in more than half of allparticipants consists of a closely spaced bipolarderivation with slightly anterior extension. Thisunderlines that the relevant frequency componentsbelong to the mu rhythm, which originates in thesensorimotor cortical area involved in hand move-ment, and not to the classical alpha rhythms.

Figure 1C illustrates the improvement of theclassification accuracy when using the most reac-tive frequency components for each participant.The distribution of classification values using opt-imized frequency bands (as obtained with SFFSfeature selection) is shown compared with the re-sults obtained with standard frequency bands (i.e.,10–12Hz and 16–24Hz). A statistical comparisonof the classification results (by paired t-test) con-firmed higher performance with optimized as com-pared with standard frequency bands(74.7%469.7%; pp0.01).

From these and previous data (reviewed inPfurtscheller and Neuper, 2001; Pfurtscheller

et al., 2005b) we can conclude that in the major-ity of novel BCI users, it is possible to distinguishbetween imagined right and left hand movementsbased on single-trial EEG signals without anyprior imagery training. An important point is,however, to optimize the used signal componentsin terms of frequency and location for each in-dividual to accommodate subject-specific varia-bility.

Mental control strategy: differential effects ofkinesthetic and visual-motor imagery

The often-observed individual differences in im-agination-related EEG changes might be partlyexplained by varieties of motor imagery (Annett,1995; Curran and Stokes, 2003; Solodkin et al.,2004). In case that there is no specific instruction,the subject may, for example, either imagine self-performed action with ‘interior view’ or, alterna-tively, imagine seeing himself or another personperforming actions in a ‘mental video’ kind of ex-perience. Whereas the first type of imagery is sup-posed to involve kinesthetic feelings and thesecond case may be primarily visual in character.

Starting from the hypothesis that the differentmethods subjects use to perform motor imageryare very likely associated with dissimilar electro-physiological activation patterns (i.e., in terms oftime, frequency, and spatial domains), we exam-ined the possible significance of the kind of im-agery for BCI control (Neuper et al., 2005). In thisstudy the instruction how to imagine action wasvaried to create either kinesthetic motor imagery(first-person process) or visual-motor imagery(third-person process). For control purposes also‘real conditions’ were included, i.e., the motor ex-ecution and visual observation of physical handmovements, respectively (Fig. 2).

Based on multi-channel EEG recordings in 14right-handed participants we applied a learningclassifier, the distinction sensitive learning vectorquantization (DSLVQ; Pregenzer and Pfurtschel-ler, 1999), to identify the relevant features (i.e.,electrode locations and reactive frequency compo-nents) for recognition of the respective mentalstates. This method uses a weighted distance func-tion and adjusts the influence of different input

0 2 3 4 5 6 7 8 time in s1

OOM - Observation of hand movementMIV - Visual-motor imagery (third-person process)ME - Motor execution (clenching a small ball)MIK - Kinesthetic motor imagery (first-person process)

Observation Imagination Motor Execution Double BeepBeep

Fig. 2. Experimental tasks and timing: the four tasks (OOM, MIV, ME, and MIK) were presented in separate runs of 40 trials: each

started with the presentation of a fixation cross at the center of the monitor (0 s). A beep tone (2 s) indicated the beginning of the

respective task: subjects should either watch the movements of the animated hand (OOM), or perform movements themselves (ME), or

imagine hand movements (MIV, MIK) until a double beep tone marked the end of the trial (7 s). A blank screen was shown during the

inter-trial period varying randomly between 0.5 and 2.5 s.

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features (e.g., frequency components) throughsupervised learning. This procedure was appliedto distinguish dynamic episodes of specific process-ing (motor execution, imagery, or observation)from hardly defined EEG patterns during rest.

The results reveal the highest classification ac-curacies, in average close to 80%, for the real-vis-ual perception and motor action, both at thecorresponding representation areas. Albeit thegreat variability between participants during theimagery tasks, the classification accuracies ob-tained for the kinesthetic type of imagery (66%)were better than the results of the visual-motorimagery (56%; pp0.01). It is important to notethat for the recognition of both the execution andthe kinesthetic motor imagery of right-hand move-ment electrodes close to position C3 provided thebest-input features (Fig. 3). Whereas the focus ofactivity during visual observation was found close

to parieto-occipital cortical areas, visual-motorimagery did not reveal a clear spatial pattern andcould not be successfully detected in single-trialEEG classification.

These data corroborate that motor imagery,specifically when creating kinesthetic feelings, canbe used to ‘produce’ movement-specific and locallyrestricted patterns of the oscillatory brain activity.Moreover, we can expect that specific instructionshow to imagine actions along with a careful usertraining may contribute to enhance activation inprimary sensorimotor cortical areas (cf. Lotze etal., 1999; DeCharms et al., 2004) and therewith, toimprove BCI control.

Impact of feedback and training

In general, when a naıve user starts to practicehand-motor imagery, a contralaterally dominant

Fig. 3. Topographical map of grand average classification accuracies (N ¼ 14) plotted at the corresponding electrode positions (linear

interpolation), separately for the four experimental conditions (ME, OOM, MIK, and MIV). Black areas indicate the most relevant

electrode positions for the recognition of the respective task. Scaling was adjusted to minimum and maximum values obtained for each

condition (ME (min/max%): 53/76; OOM (min/max%): 56/77; MIK (min/max%): 51/64; and MIV (min/max%): 51/61).

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desynchronization pattern is found. In the courseof a number of training sessions, in which he or shereceives feedback about the performed mentaltask, changes of the relevant EEG patterns can beexpected. In the case of a simple 2-class motorimagery task with imagination of right vs. lefthand movement, an ipsilateral localized ERS oftendevelops as the number of training sessions in-creases (Pfurtscheller and Neuper, 1997; Neuper etal., 1999). Such a ‘contralateral ERD/ipsilateralERS’ pattern is associated with an increase in theclassification accuracy of single-EEG trials. Thedata example of a representative subject in Fig. 4displays the comparison of ERD/ERS curves(11–13Hz) at two electrode positions (C3 andC4) between an initial session without feedbackand a later session with classifier feedback. Itcan be clearly seen that initially one-sided handmotor imagery elicited only ERD patterns with aclear dominance over the contralateral hemi-sphere. After feedback training, however, an ipsi-lateral ERS became apparent. The classificationaccuracy achieved in the ‘‘training without feed-back’’ session was 87%, after feedback training

the brain patterns could be classified with 100%accuracy.

The enhancement of oscillatory EEG activity(ERS) during motor imagery is a very importantaspect in BCI research and, presumably, requirespositive reinforcement. Feedback-regulation of thesensorimotor oscillatory activity was originally de-rived from animal experiments, where cats wererewarded for producing increases of the sensori-motor rhythm (SMR; Sterman, 2000). Also in BCIresearch it has been documented that human sub-jects can learn to enhance or to suppress rhythmicEEG activity when they are provided with infor-mation regarding the EEG changes (e.g., Wolpawet al., 1991; Neuper et al., 1999; Wolpaw andMcFarland, 2004; for a more general review onneurofeedback see Chapter 27, this volume). Theprocess of acquiring control of brain activity (i.e.,to deliberately enhance patterns of oscillatory ac-tivity) can therefore be conceptualized as an im-plicit learning mechanism involving, among otherprocesses, operant learning.

The main rationale of (classifier-based) BCItraining is, however, to take advantage of both the

Fig. 4. Band power (11–13Hz) time courses 795% confidence interval displaying ERD and ERS from training session without

feedback (left) and session with feedback (right). Data from one able-bodied subject during imagination of left and right hand

movement. Grey areas indicate the time of cue presentation.

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learning progress of the human user and, simulta-neously, the ‘learning capability’ of the system(Pfurtscheller and Neuper, 2001). Initially, thecomputer has to learn to recognize EEG patternsassociated with one or more states of mental im-agery. When an appropriate classifier is available,the online classification process can start and feed-back can be provided to enable learning of theuser, thereby enhancing the target EEG patterns.As a result of feedback training, the EEG patternsusually change, but not necessarily in the desireddirection (i.e., divergence may occur). For thisreason, the generation of appropriate EEG feed-back requires dynamic adjustment of the classifierand of the feedback parameters. (The issue of on-going interaction between the user and the systemis also dealt with in Chapter 26.)

To keep the training period as short as possible,a well thought-out training protocol is essential.In this context, two aspects are crucial, (i) the ex-act manner of how the brain signal is translatedinto the feedback signal (i.e., information con-tent of the feedback; for advantages of providingcontinuous or discrete feedback, see McFarlandet al., 1998; Neuper et al., 1999) and (ii) the typeof feedback presentation (e.g., visual feedbackappears superior to auditory feedback, see Phamet al., 2005). In any case, the influence of the

feedback on the capacity for attention, concentra-tion, and motivation of the user, all aspects thatare closely related to the learning process, shouldbe considered (see also Pineda et al., 2003).

Mode of operation: synchronous vs. asynchronousBCI protocols

When designing a BCI system for a concrete ap-plication, two different operating modes are pos-sible: cued or synchronous and non-cued orasynchronous. In the case of a synchronous BCI,the mental task has to be performed in predefinedtime windows following a visual or auditory cuestimulus. The time periods during which the usercan exert control, for example, by ‘producing’ aspecific mental motor imagery, are determined bythe system and the processing of the data is limitedto these fixed periods. The majority of work incurrent BCI research is based on this synchronousmode (for a review, see Wolpaw et al., 2002).

An asynchronous protocol, in contrast, allowsthe user to intend an operation independently of anexternal cue stimulus. This implies that the timewindows of intended mental activities are un-known, and therefore the signal has to be analyzedcontinuously. Asynchronous BCIs have not only todeal with the discrimination between distinct motor

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imagery related brain patterns (‘control-states’), butalso the main challenge is to handle the ‘non-control-states’. In between the user’s intentions tocommand the device, similar EEG patterns asduring the control-state can occur unintentionally,leading to false-positive reactions of the system. Forreal-world applications, when the user needs fullcontrol over timing and speed of BCI operation, theasynchronous communication mode is mandatory.First asynchronous BCIs based on motor imagery(e.g., hand or finger motor imagery) have beendeveloped for cursor control (Birch et al., 2002)and for the control of a spelling device (Millan andMourino, 2003; Scherer et al., 2004).

Usefulness of an EEG-based BCI to establish

communication in severely paralyzed patients

Most of the currently available BCI systems havebeen developed and tested in able-bodied users inthe laboratory, whereas only a few research groupsreported experience in applying and adapting aBCI in severely paralyzed patients (Birbaumeret al., 1999; see also Chapter 24). To explore thepractical usefulness of a BCI for communicationand control and to investigate how long-term BCItraining affects the EEG signals used, is thereforeof utmost importance from the clinical point ofview.

Table 1 presents an overview of the patients whoparticipated in long-term single case studies withthe Graz-BCI. It has to be kept in mind thattraining patients at their homes is an extremelychallenging task for all people involved: the pa-tient, his or her social environment (e.g., familymembers and/or caregivers) and the BCI researchgroup (Neuper et al., 2003). All participants in-cluded in our ‘‘home BCI studies’’ were severelydisabled and had very little or no residual volun-tary muscle control. Included were people withlate-stage amyotrophic lateral sclerosis (ALS) (pa-tient H.D.), severe cerebral palsy (K.I), musculardystrophy (T.K.), and high-level spinal cord inju-ries (T.S. and H.K.). Two of them were artificiallyventilated (T.K., H.D.). To realize a BCI system tobe used at the patient’s home, we were workingwith a portable, remotely controlled system (for

details see Guger et al., 2001). An important pre-requisite was the use of a ‘telemonitoring’ equip-ment (Muller et al., 2003a), which allowed foronline supervision and procedure adaption fromthe laboratory, even from long distances from thepatient. The actual training was carried out bytrained caregivers at the patient’s home.

A ‘Virtual Keyboard’ for spelling

Completely paralyzed patients without any con-scious control of muscle activity can communicatewith their environment when, through the use ofEEG signals, an electronic spelling device is con-trolled (Birbaumer et al., 1999). An important ap-plication of the Graz-BCI is the so-called ‘VirtualKeyboard’, a spelling program based on the de-tection and classification of motor imagery relatedEEG patterns (Obermaier et al., 2003). The basicdevice allows the selection of letters from an al-phabet by making a series of binary decisions. Thismeans that the user has to learn to reliably repro-duce two different EEG patterns (classes). Startingwith the complete alphabet displayed on a screen,subsets of decreasing size, containing the targetletter, are successively selected until the desiredletter is one of two options. The dichotomousstructure includes five consecutive levels of selec-tion and two further levels of confirmation andcorrection. A bar, extending either to the left or tothe right hand side of the screen, indicating thefirst and the second subset, respectively, is pre-sented as feedback. The user is required to spellpredefined words presented by the experimenter byselecting the appropriate letter subgroup by motorimagery (‘copy spelling’). A measure for the com-munication performance is the spelling rate s,given as correctly selected letters per minute. Inhealthy subjects spelling rates between 0.7 and 1letters/min could be achieved when using a triallength of 8 s (Obermaier et al., 2003). In the asyn-chronous spelling program introduced more re-cently (Scherer et al., 2004), the time for eachselection is set by the user. This may considerablyspeed up the selection process, but needs thedifferentiation of three classes.

In the last years, we trained three patients (K.I.,T.K., and H.D.) successfully to operate the 2-class

Table 1. Overview of patients trained with the Graz-BCI for real-life applications

Patient Age, sex Diagnosis Motor, speech

functions

Electrode

location,

frequency

band(s)

Mental

strategy

Number of

training days

(sessions)

Class.

accuracy, first

10 sessions

Class.

accuracy, last

10 sessions

BCI

application,

training result

K.I. 32, male Cerebral palsy Tetraplegic

(spastic), no speech,

residual muscle

activity of the

upper right arm

C3, 20–30Hz Right hand

vs. relaxing

44 (139)a 61%75.3 69%75.4

max. 80%

Virtual

keyboard,

copy spelling

T.K. 33, male Muscle dystrophy type

Duchenne, artificially

ventilated

Tetraplegic, verbal

communication

with technical aid

possible

C3, C4

8–12Hz,

15–24Hz

Right vs. left

hand

23 (83)a 65%74.6 85%74.2

max. 99%

Virtual

keyboard,

copy spelling

H.D. 60, male ALS, since 5 years

artificially ventilated

Nearly completely

paralyzed, very

weak head and eye

movement

C3, C4

8–12Hz,

18–30Hz

Right vs. left

hand/body

17 (82)a 49%710.2 83%76.1

max. 98%

Virtual

keyboard,

free spelling

T.S. 30, male SCI, level C4

(incomplete), level C5

(complete)

Residual muscle

function in both

shoulders, active

elbow flexion left

C3, Cz

10–12Hz,

15–19Hz

Feet vs. right

hand

4 months

(62)b63%71.9 88%78.5

Neuroprosthesis,

surface FES, grasping

H.K. 42, male SCI, level C5

(complete)

Residual muscle

function in both

shoulders, active

elbow flexion

Cz, C4

12–14Hz,

18–22Hz

Left hand vs.

feet

3 (29)c – 71%d

Neuroprosthesis

implanted Freehand

(R), grasping

aOne session ¼ 80 trials. bOne session ¼ 160 trials. cOne session ¼ 40 trials. dBest four sessions.

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virtual keyboard. All of them achieved sufficientperformance rates (70–85%) for copy spelling (seeTable 1), which allowed them to write with a rateof approximately 1 letter/min. This was first evi-dence that a BCI based on movement-related os-cillatory EEG changes, induced by motor imagery,can be operated by patients suffering from neuro-logical diseases affecting various functional com-ponents of the central nervous system (for adetailed case report, see Neuper et al., 2003).

Case study in ALS-patient

As an example, we shortly report here the case of a60-year-old male patient (H.D.) who was diag-nosed with ALS. At the time the BCI-trainingstarted, he was already artificially ventilated formore than 5 years, totally paralyzed and had al-most lost his ability to communicate. The goal ofthe training for the patient was to produce twodistinct EEG patterns by using an imagery strat-egy, to gain a control option for operating the vir-tual keyboard. The EEG signal (band pass5–30Hz) was recorded from the left and right sen-sorimotor area using two bipolar derivations. Toset-up the online system, initial training sessionswere performed without feedback. According to

Fig. 5. Left side: picture of a patient suffering from ALS during feedb

shown. Right side: examples of single EEG traces and ERD/ERS tim

Hz components.

the standard protocol of the Graz-BCI (as de-scribed earlier in this Chapter), the training con-sisted of the repetitive process of cue-based motorimagery (i.e., left vs. right hand imagery) trials.Based on the collected screening data, the feedbackfor online experiments was computed by applyingthe LDA to logarithmic band power features ex-tracted from the ongoing EEG. Two frequencybands (8–12 and 18–30Hz) were selected and ex-tracted from each EEG channel. With the resulting4-band power features individual LDA classifierswere trained at different time points (every 0.5 s)within the motor imagery period. The classifierobtained at the time point with the highest accu-racy was chosen for the online feedback training.The so-called ‘basket paradigm’ (for details seeKrausz et al., 2003) was employed to train the pa-tient to reliably reproduce two different EEG pat-terns. This paradigm requires continuous (1-dimensional) cursor control to direct a ball, fall-ing with a constant speed from the top of thescreen, into one of two baskets (the target is indi-cated in each trial) positioned at the bottom of thescreen (see Fig. 5, left side). The classification resultwas mapped to the horizontal position of the ball.

In 17 training days the patient performed 82 runswith the basket paradigm. The effectiveness of the

ack training. In the upper right corner the ‘basket’-paradigm is

e/frequency maps. The right map displays ipsilateral ERS of 11-

403

training was suggested by the significant increase ofthe classification accuracy from random level at thebeginning to an average classification rate of 83%(see Table 1). At the end of the reported trainingperiod, this patient was able to voluntarily producetwo distinct EEG patterns: one is characterized bya broad-banded ERD, the other by a narrow-banded ERS in the form of induced 10-Hz oscil-lations (ERS) (Fig. 5, right side). The BCI-controlachieved enabled the patient to use the 2-class vir-tual keyboard. After several training runs withcopy spelling, the patient finally succeeded in freespelling: as a first utterance, he voluntarily spelled‘MARIAN’, the name of his caregiver.

To summarize, it could be shown that a com-pletely paralyzed ALS patient, who had lost al-most all voluntary muscle control, learned how toenhance and suppress specific narrow frequencycomponents of the sensorimotor EEG. By using amotor imagery strategy, the patient could producea clear-cut ERS pattern of 10-Hz components insensorimotor cortex, similar to able-bodied sub-jects. These results document that paralyzed pa-tients may retain the ability to generate neuralsignals for motor control, although their motorpathways may be severely interrupted.

Modulation of oscillatory brain signals for the

restoration of movement

From the very beginning, the development of BCIsystems grounded on the idea to bypass inter-rupted motor pathways and therewith, allow res-toration of movement in paralyzed patients.However, only a few studies have addressed thesuitability of non-invasive, EEG-based BCIs forneuroprosthetic applications (Pfurtscheller et al.,2000a, 2003, 2005a; Muller-Putz et al., 2005; for areview, see Muller-Putz et al., 2006). Neuropros-theses aim to restore lost motor function throughelectrical stimulation of paralyzed muscles. A res-toration of motor functions (i.e., grasping) by us-ing functional electrical stimulation (FES) ispossible, if the nerves connecting the ventral rootsof the spinal cord to the peripheral muscle are stillintact (Reilly and Antoni, 1992). By placing eithersurface or subcutaneous electrodes near the motor

point of the muscle and applying stimulationpulses, action potentials are elicited leading tothe contraction of the innervated muscle fibers. Onthis basis, FES artificially compensates for the lossof voluntary muscle control.

The main target groups for this application arepatients with an injury of the high spinal cord,which leaves them with only few residual volun-tary movements. So far, preserved movements, forexample, of the shoulder or the tongue, are used tooperate the restored function (e.g., hand grasp).Since these traditional control methods come totheir limits concerning degrees of freedom and useracceptance, a BCI, providing a hands-free controloption by using signals derived directly from theuser’s brain, may have significant potential as afuture control option (Heasman et al., 2002;Pfurtscheller et al., 2003; Muller-Putz et al.,2005; see Fig. 6).

The operation of neuroprostheses could bebased on the real-time detection of motor intentsor imaginations in continuous EEG recordings.An important point to consider is, however, thatthe restoration of motor activity with FES affectsthe ongoing EEG resembling to voluntary handmovements (Muller et al., 2003b). Because of thehigh equivalence between active and stimulation-induced movements, and because similar brainsignals are reactive to both movement and motorimagery, it is very likely that limb movement or themovement of the neuroprosthesis may interferewith brain signals used by the BCI. Thus, it can beexpected that a BCI using hand motor imagerycould only operate a neuroprosthesis of the upperextremity, if the imagery used was of the handopposite to the neuroprosthesis, and only if thehand/arm without the neuroprosthesis was keptimmobilized. As an alternative, foot motor im-agery appears especially suitable to generate EEG-based commands for the restoration of hand grasp.

EEG-based control of neuroprostheses in SCIpatients

Presently, we have experience with EEG-basedcontrol of neuroprosthetic devices in two tetra-plegic patients (T.S. and H.K.) suffering from spi-nal cord injury (SCI). Both learned to operate a

Fig. 6. Left side: principle of BCI-based neuroprosthetic control. A lesion of the spinal cord leading to an interruption of efferent and

afferent fiber tracts is artificially bridged by coupling a BCI-system with a neuroprosthesis. Note that the feedback provided in this

application is exclusively visual, i.e., the observation of the moving hand. Right side: pictures of patient T.S. showing him watching his

grasping hand (upper) and lifting a drinking glass with the BCI-controlled neuroprosthesis (lower).

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self-paced, asynchronous BCI (‘brain-switch’)based on the modulation of oscillatory EEG com-ponents by mental motor imagery. With this‘brain-switch’ they were able to operate a ne-uroprosthesis and, in this way, regain control oftheir grasp function without any muscular activity.

For one patient (T.S., 30 years old) the handgrasp function of his left hand was restored with aFES using surface electrodes. This tetraplegic pa-tient participated in studies with the Graz-BCI forseveral years. During a first BCI-training period of4 months in 1999 he learned to induce trains of 17-Hz oscillations over the vertex (electrode Cz) byfoot motor imagery (Pfurtscheller et al., 2000a).Since the patient learned to generate this brainpattern at will, the asynchronous operating modecould be used for control purposes. By estimatingthe band power in the significant narrow frequencyband (15–19Hz) and by applying simple thresholdclassification the foot motor imagery related brainpattern could be detected with an accuracy of al-most 100%. Of special interest is that the patient

retained this ‘skill’ over years, so that he could usethe BCI to control the FES in 2003. The obtainedtrigger signal was used to switch between graspphases implemented into a stimulation unit (seeFig. 7B). Three FES channels, provided by surfaceelectrodes placed at the forearm and hand, wereused for grasp restoration. Every time the patientwanted to grasp an object, he could consecutivelyinitiate (‘brain-switch’) phases of the grasp. In thecase of a lateral grasp three phases had to be ex-ecuted in the order: (i) finger and thumb extension(hand opens), (ii) finger flexion and thumb exten-sion (fingers close), and (iii) finger and thumb flex-ion (thumb moves against closed fingers). With thisgrasp function he was able to hold, for example, aspoon (Pfurtscheller et al., 2003). By repositioningthe electrodes, establishing a kind of palmar grasp,he was able to hold and use a drinking glass(Pfurtscheller et al., 2005a). The EEG (0.5–30Hz)was recorded using two bipolar channels with fourelectrodes attached in a distance of 2.5 cm anteriorto and posterior to C3 and Cz. Features for

Fig. 7. A: Average of LDA outputs during grasping in patient T.S. B: Corresponding band power features to A. C: Single foot motor

imagery EEG trials from channel Cz during grasping (modified from Muller-Putz et al., 2006).

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real-time signal processing were logarithmic bandpower time series. For imagery detection an LDAclassifier was used (Pfurtscheller et al., 2003).

Figure 7 shows data of an asynchronous exper-iment where the patient controlled his grasp byfoot motor imagery. In Fig. 7A the averaged LDAoutputs during grasping is presented. Band powertime courses of the two bipolar channels C3 andCz for the bands 10–12 and 15–19Hz are shown inFig. 7B. It can clearly be seen that the 17Hz bandpower of channel Cz represents the most powerfulfeature to produce reliable LDA output. Fig. 7Cillustrates examples of single EEG traces derivedfrom position Cz during a grasp sequence. TheBCI system used for grasp control, based only on asimple threshold detector, is described in detailelsewhere (Pfurtscheller et al., 2005a).

The second patient (42 years old, complete le-sion below C5) had a Freehands system (Keithet al., 1989) implanted in his right hand and arm atthe Orthopedic University of Heidelberg in 2000.This neuroprosthesis is usually controlled by a

shoulder joystick and allows the user to performdifferent grasp patterns (Peckham et al., 2001). Incontrast to the long-term study in patient T.S., thetraining and implementation of the closed loopbetween the BCI and the Freehand (R) system wasperformed in only 3 days at the patient’s home. Atthe beginning, the patient was asked to imaginefoot and left hand movements during EEG re-cordings to find the best mental strategy suited forthe operation of the neuroprosthesis. Based on therecorded data (from channels Cz and C4), themost reactive frequency bands were selected(12–14 and 18–22Hz), a classifier set up, andtraining sessions with online feedback performedas described earlier. The classifier was then used inan asynchronous paradigm for free training. Be-cause of a significant ERD during left hand motorimagery, it was decided to use this type of imag-ination for switching. After the free training wascompleted, the classifier output of the BCI wascoupled with the Freehand (R) system. In otherwords, the BCI system emulated the shoulder

Fig. 8. A: Average of LDA output during the grasp-test in patient H.K. B: Corresponding averaged band power features of electrode

positions Cz and C4 (modified from Muller-Putz et al., 2006).

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joystick, which the patient usually used. With thisBCI-controlled neuroprosthesis he could success-fully perform skilful movements, as required by ahand grasp performance test (for details seeMuller-Putz et al., 2005).

Data examples obtained during the hand grasp-release test are displayed in Fig. 8. The task was torepetitively move one paperweight from one placeto another within a time interval of 3min.The pa-tient achieved 16 switches by consecutively imag-ining left hand movements. The averaged LDAoutput during these imaginations (A) togetherwith the corresponding band power features(12–14 and 18–22Hz) are shown (B).

Summarizing, the realization of a ‘thought-con-trolled’ device, which may help humans with par-alyzed limbs to restore their grasp function is notunreachable any more. The reported case studiesillustrate how an EEG-based brain-switch can beimplemented and effectively used for the control ofa hand grasp neuroprosthesis. There are two mainrequirements that have to be fulfilled for the cou-pling of a BCI with such a device: (i) the patienthas to be able to produce significant EEG changeswith motor imagery and, (ii) in a mutual adapta-tion process, the seamless integration of the ne-uroprosthesis with the BCI-system has to beachieved. In the near future, the use of wirelessEEG recordings, online artifact detection and moredegrees of freedom, especially for proportionalcontrol, may contribute to further improvementsof such ‘thought-controlled’ neuroprostheses.

Conclusion

As a result of 15 years of research dedicated to thedevelopment of a non-invasive, EEG-based BCIsystem, it was demonstrated that ERD/ERS patternsrelated to certain types of motor imageries couldbe recognized successfully in real time. Based on thisground, the feasibility of using the ongoing oscilla-tory EEG signal for communication and movementrestoration in paralyzed patients was established.

A clear challenge for the future is to realize moreeffective BCI control paradigms, offering, for in-stance, 3-dimensional, proportional control over aneuroprostesis. This should be attainable either byan enhancement of the classification accuracy orthe simultaneous discrimination between three ormore EEG patterns (Wolpaw and McFarland,2004). To obtain high accuracy in single-trial clas-sifications, it is further important to use efficientfeature extraction and classification methods (fora review, see Pfurtscheller et al., 2006b) andto exploit the most relevant features from theEEG for each participant. Another critical point isto search for spatio-temporal EEG patterns dis-playing task-related synchronization or ERS(Pfurtscheller et al., 2006a). Recognition of suchERS phenomena is a prerequisite for a high hitrate and a low false positive detection rate in anasynchronous BCI. More extensive work is alsoneeded to specify the mental task and to optimizethe user training. So, for example, kinestheticimagery (i.e., remembering the feeling of hand

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movement) has led to better results than visuo-motor imagery, realized either by visualization ofone’s own or another’s hand movement (Neuper etal., 2005). Whether some kind of mental trainingor experience with relaxation techniques may fur-ther contribute to better BCI performance is amatter of current research.

Acknowledgments

The authors would like to acknowledge the moti-vation and patience of the participating patientsduring their weeks and months of training. Theauthors also thank Dipl. Ing. Rudiger Rupp fromthe Orthopedic University of Heidelberg, for hisclose collaboration in terms of neuroprostheticdevices and setting up muscle training in patientT.S., and Dr. Heinz Lahrmann, Department ofNeurology and Ludwig-Bolzmann Institute forNeurooncology, Kaiser Franz Josef Hospital, Vi-enna, for his support of the BCI training with pa-tient H.D. This work was supported in part by the‘‘Fonds zur Forderung der WissenschaftlichenForschung’’ in Austria, project P16326-B02.

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