title: High-resolution movement EEG classification.
abstract: The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94-100%), but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related) EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.
authors.affiliation: Biosignal Laboratory, Department of Circuit Theory, Faculty of Electrotechnical Engineering, Czech Technical University in Prague, Technická 2, Prague 16627, Czech Republic. stastnj1@seznam.cz
authors.forename: Jakub
authors.initials: J
authors.lastname: Stastný
authors.forename: Pavel
authors.initials: P
authors.lastname: Sovka