PATTERN RECOGNITION OF EEG SIGNAL DURING MOTOR IMAGERY BY USING SOM

Electroencephalograph (EEG) recordings during Tight and left hand motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It, can be used by e.g., handicap users with amyotrophic lateral sclerosis (ALS). The conventional method purposes the recognition of right hand and left, hand motor imagery. In this work, feature extraction method based on Self Organizing Maps (SOM) using auto-regressive (AR) spectrum was introduced to discriminate the EEG signals recorded during Tight hand, left hand and foot motor imagery. Map structure is investigated in order to develop a BCI system which extracts physically meaningful information directly relevant to motor imagery. The analysis methods of EEG signals are discussed through the experimental studies.