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MOTOR IMAGERY
Sensorimotor rhythms (SMR)
Detected in the sensorimotor area
Somatosensory cortex
Motor cortex
Mu rhythms: 8-12 Hz
Beta rhythms: 12-30Hz
ECoG can also use gamma rhythms (30-80Hz)
Monday, March 19, 2012
MOTOR IMAGERY
ERD (Event Related Desynchronization)
Planning and execution of hand/finger movements block (desynchronize) mu rhythms
ERS (Event Related Synchronization)
Inhibition of movements synchronizes mu rhythms
Foot and tongue movements enhance mu rhythms
ERD/ERS is generally observed for the contralateral movements
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MOTOR IMAGERY
Does not need external stimuli
Does need long training
Users learn the best imagery
Closed-loop feedback plays an important role in learning
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SIGNAL PROCESSING
Preprocessing
Feature Extraction
Detection/Classification
- Simplifies signals: e.g. filtering- Improves the signal-to-noise ratio (SNR)
- Extract features relevant to control parameters using mathematical methods- e.g. Amplitudes, Frequencies, Firing rates, ...
- Detection: detects specific patterns from ordinary patterns- Classification: classifies a given pattern to one of many variables
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SIGNAL PROCESSING
Synchronous BCIs
Cue-paced
Operates a BCI with a fixed time frame
Asynchronous BCIs
Self-paced
Operates a BCI whenever the user wants
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PERFORMANCE MEASURES
Classification Rate (= 1 - Error Rate)
# correct / total attempts
Letters / minute for a speller
Information transfer rate (ITR)
Depends on classification accuracy, performance time, and # classes
Bits / minute
Ball park: 30 bits/min ~ 90 bits/min (?)
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CONFUSION MATRIX
Estimated As Positives
Estimated As Negatives
Positives True Positives (TP)
False Negatives (FN)
Sensitivity = TP / (TP + FN)
Negatives False Positives (FP)
True Negatives (TN)
Specificity = TN / (TN + FP)
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BCI OUTPUT
Discrete output (Discrete State Variables)
Output = one of N possible values
e.g. “go” / “stop”
Continuous output (Continuous State Variables)
Output = continuous values, probably within a finite or infinite range
e.g. position on a 2D space
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Brain–Computer Interfaces: A Gentle Introduction 17
Fig. 7 Examples of BCI applications. (a) Environmental control with a P300 BCI (see chapter“The First Commercial Brain–Computer Interface Environment”), (b) P300 Speller (see chapter“BCIs in the Laboratory and at Home: The Wadsworth Research Program”), (c) Phone numberdialling with an SSVEP BCI (see chapter “Practical Designs of Brain–Computer Interfaces Basedon the Modulation of EEG Rhythms”), (d) Computer game Pong for two players, E) Navigation ina virtual reality environment (see chapter “The Graz Brain–Computer Interface”), (f) Restorationof grasp function of paraplegic patients by BCI controlled functional electrical stimulation (seechapter “Non invasive BCIs for neuroprostheses control of the paralysed hand”)
letter and writing the message “water, please” or just “water”. Since this is a wishthe patient may have quite often, it would be useful to have a special symbol orcommand for this message. In this way, the patient can convey this particular mes-sage much faster, ideally with just one mental task. Many more short cuts mightallow other tasks, but these short cuts lack the flexibility of writing individual mes-sages. Therefore, an ideal BCI would allow a combination of simple commands to
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CONTROL LEVEL
Low-level
Process-oriented control
e.g. move a cursor in 45 degree with a speed of 2cm/s at this time instant (for 50ms)
More specific
High-level
Goal-oriented control
e.g. move a cursor to a target #4
All the details are managed by an actuator
More general
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18 B. Graimann et al.
convey information flexibly and short cuts that allow specific, common, complexcommands.
In other words, the BCI should allow a combination of process-oriented (or low-level) control and goal-oriented (or high level) control [41, 42]. Low-level controlmeans the user has to manage all the intricate interactions involved in achieving atask or goal, such as spelling the individual letters for a message. In contrast, goal-oriented or high-level control means the users simply communicate their goal to theapplication. Such applications need to be sufficiently intelligent to autonomouslyperform all necessary sub-tasks to achieve the goal. In any interface, users shouldnot be required to control unnecessary low-level details of system operation.
This is especially important with BCIs. Allowing low-level control of awheelchair or robot arm, for example, would not only be slow and frustratingbut potentially dangerous. Figure 8 presents two such examples of very complexapplications.
The semi-autonomous wheelchair Rolland III can deal with different input mod-alities, such as low-level joystick control or high-level discrete control. Autonomousand semi-autonomous navigation is supported. The rehabilitation robot FRIEND II(Functional Robot Arm with User Friendly Interface for disabled People) is a semi-autonomous system designed to assist disabled people in activities of daily living.It is system based on a conventional wheelchair equipped with a stereo camera sys-tem, a robot arm with 7 degrees-of-freedom, a gripper with force/torque sensor, asmart tray with tactile surface and weight sensors, and a computing unit consist-ing of three independent industrial PCs. FRIEND II can perform certain operations
Fig. 8 Semi-autonomous assistive devices developed at the University of Bremen that includehigh level control: Intelligent wheelchair Rolland III, and rehabilitation robot FRIEND II (modifiedfrom [35])
Rolland III FRIEND II
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CURRENT LIMITS OF BCIS
Reliability
Nonstationary brain signals
Sensitive to noise
Bandwidth
Low information transfer per second
Less competitive than conventional techniques
Healthy subjects: keyboards, mouse, speech, ...
Disabled subjects: eye-tracker, head mouse, ...
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WILL NORMAL PEOPLE USE BCIS?
Yes if,
combined with intelligent systems
bandwidth continues to improve
used as an augmented interface
more accessible by general researchers (e.g. HCI fields, robotics fields, ...)
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APPLICATION IN NEUROFEEDBACK
A BCI can be thought as the most advanced neurofeedback system
A BCI can be applied to neurorehabilitation fields where neurofeedback is necessary
ADHD
Autism
Epilepsy
Stroke
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