This paper examines the performance of four classifiers for Brain Computer Interface (BCI) systems based on multichannel EEG recordings. The classifiers are designed to distinguish EEG patterns corresponding to performance of several mental tasks. The first one is the basic Bayesian classifier (BC) which exploits only interchannel covariance matrices corresponding to different mental tasks. The second classifier is also based on Bayesian approach but it takes into account EEG frequency structure by exploiting interchannel covariance matrices estimated separately for several frequency bands (Multiband Bayesian Classifier, MBBC). The third one is based on the method of Multiclass Common Spatial Patterns (MSCP) exploiting only interchannel covariance matrices as BC. The fourth one is based on the Common Tensor Discriminant Analysis (CTDA), which is a generalization of MCSP, taking EEG frequency structure into account. The MBBC and CTDA classifiers are shown to perform significantly better than the two other methods. Computational complexity of the four methods is estimated. It is shown that for all classifiers the increase in the classifying quality is always accompanied by a significant increase of computational complexity.
Motor recovery in post-stroke and post-traumatic patients using exoskeleton controlled by the brain-computer interface (BCI) is a new and promising rehabilitation procedure. Its development is a multidisciplinary research which requires, the teamwork of experts in neurology, neurophysiology, physics, mathematics, biomechanics and robotics. Some aspects of all these fields of study concerning the development of this rehabilitation procedure are described in the paper. The description includes the principles and physiological prerequisites of BCI based on motor imagery, biologically adequate principles of exoskeleton design and control and the results of clinical application.
The paper examines sources of brain activity, contributing to EEG patterns which correspond to motor imagery during training to control brain-computer interface. To identify individual source contribution into electroencephalogram recorded during the training Independent Component Analysis was used. Then those independent components for which the BCI system classification accuracy was at maximum were treated as relevant to performing the motor imagery tasks, since they demonstrated well exposed event related de-synchronization and event related synchronization of the sensorimotor μ-rhythm during imagining of contra- and ipsilateral hand movements. To reveal neurophysiological nature of these components we have solved the inverse EEG problem to locate the sources of brain activity causing these components to appear in EEG. The sources were located in hand representation areas of the primary sensorimotor cortex. Their positions practically coincide with the regions of brain activity during the motor imagination obtained in fMRI study. Individual geometry of brain and its covers provided by anatomical MR images was taken into account when localizing the sources.