On feature selection for brain computer interfaces

Currently, Brain Computer Interfaces (BCI) are of limited practical use requiring prolonged user training. BCI usability can improve by developing methods for EEG feature selection that allow for a more intuitive link between user intention and device control and which can provide multiple degrees of freedom in command selection. EEG was recorded from 28 electrodes forming an array over the left sensorimotor cortex in normal subjects with approval of a Local Ethics Committee. Right wrist muscle EMG was also recorded. Subjects viewed a PC monitor randomly presenting a set of five spatially distinct target positions corresponding to neutral, flexed, extended, ulnar and radial deviations of the wrist. When presented with a new target subjects were required to move (or imagine move) their wrist to the target as fast as possible creating datasets of repeats of 20 different movement (or imagined movement) combinations. Continuous wavelet transform was used to extract spatio-temporal characteristics of the EEG signals relative to time of movement initiation or target appearance in case of imagined motion. Using PCA, 10 principal components generated 95% correct classification of pre-movement EEG with actual direction of motion. During imagined movement classification success reduced to 67%. In contrast, SVM classification of the imagined movement data outperformed PCA with up to 87% correct classification for intended direction of movement. During imagined movement SVM outperforms PCA. However, SVM is computationally demanding. We conclude that a hybrid method combining merits of both PCA and SVM may have potential for use in BCI intention detection