g.BCIsys

Brain-computer interface A Brain-Computer Interface (BCI) provides a new communication channel between the human brain and the computer. Mental activity leads to changes of electrophysiological signals like the electroencephalogram (EEG). The BCI system detects such changes and transforms it into a control signal which can, for example, be used to play a simple video game like in the picture above. One of the main goals is to enable completely paralyzed patients (locked-in syndrome) to communicate with their environment.

Two Learning Systems

An interesting question for the development of a BCI is how to handle two learning systems: The machine should learn to discriminate between different patterns of brain activity as accurate as possible and the user of the BCI should learn to perform different mental tasks in order to produce distinct brain signals.

BCI research makes high demands on the system and software used. Parameter extraction, pattern recognition and classification as well as the generation of neurofeedback for a successful training of the user has to run in real-time.

Complete Development and Research System

g.tec provides complete MATLAB-based development/research systems including all hard- and software components needed for data acquisition, real-time and off-line data analysis, data set classification and for providing neurofeedback.

The systems are available with 8 - 32 channels as a notebook system and with 8 - 64 channels as PC-based system.

Scientific Approaches

During the past decade many groups all over the world intensified their work in the field of BCI-research. The different methods published in international scientific journals display the wide range of possible solutions for the problem. The following link provides an overview on some of the most prominent methods and authors working on this hot topic: State of the Art in BCI research

To support your start into the fascinating world of Brain-Computer Interface research review the literature here.

Some of the most commonly used strategies to realize a BCI are:

• Imagery of movements of different limbs cause changes in oscillatory EEG activity over sensorimotor areas of the central cortex. These changes can be classified by weighting spectral parameters of different frequency bands for different electrode positions.
• Slow shifts of cortical potentials occur when a subject performs an imagery of expecting an event (like waiting for a traffic light turning to green). The resulting DC-shift can be used for biofeedback to improve the training effects and to generate a control signal for communication.
• Also other mental tasks such as mental arithmetic, mental cube rotation or attention versus relaxation are used to produce characteristic changes of EEG patterns. One attempt has also been not to guide the subjects with any strategy but use specific EEG-biofeedback, so that the user attempts to find his/her own strategy for producing the required changes in the EEG.
• An other method uses steady-state visually evoked potentials (SSVEP) from flickering light sources. Directing attention to a source with a specific flicker frequency enlarges evoked components in the EEG with the same frequency.

It can be stated that none of all the methods used in BCI research yields perfect results but the performance was significantly improved by new parameter-extraction algorithms and pattern-recognition/classification methods. The usability of a BCI has to be evaluated with respect to the following aspects:

• accuracy (classification error, hits vs. false, false positives, ...)
• information transfer (decision speed, bit/min, ...)
• number of classes (idling vs. activation of 1 class, 2 or more different classes, ...)
• operation mode (synchronous: predefined decision intervals, asynchronous: free decision time)
• intended application (spelling device, control of orthotic/prosthetic device, environmental control)

Example Paradigm

g.BCIsys comes with a ready-to-use example BCI-paradigm based on changes in oscillatory EEG activity induced by two different types of motor imageries.

Step 1 (initial training):

Based on a cue (arrow on the screen pointing to the left or to the right) the subject performs left and right hand movement imageries (duration 3-4 seconds). To train the classifier between 40 and 160 trials are recommended. EEG should be recorded from electrode positions C3 and C4.

Step 2 (offline analysis and classifier generation):

The recorded data are processed with g.BSanalyze. Alpha and beta band power parameters for both EEG channels are computed to build the feature matrix. Linear discriminant analysis is used for classification and cross-validation shows the usability of the best classifier.

Step 3 (training with neuro-feedback):

If cross-validation results yield a classification error below approx. 20 %, the classifier can be used to generate neuro-feedback for further training. For this case data are online classified and the result is graphically presented to the subject as a horizontal bar on the screen deflecting to the right if a right hand motor imagery is detected and deflecting to the left if a left hand motor imagery is detected.

Step 4 (classifier update):

The continuous feedback should help the subject to train the motor imageries leading to a correct classification. To improve the performance the classifier should be updated after some successful sessions. A new classifier can also be computed from the data of a feedback session. Offline analysis of the recorded data supports feature optimization.

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Cortech Solutions

Updated: 12-Jul-2007