ADAPTIVE AUTOREGRESSIVE MODELING USED FOR SINGLE-TRIAL EEG CLASSIFICATION

An adaptive autoregressive (AAR) model is used for analyzing event-rel ated EEG changes. Such an AAR model is applied to single EEG trials of three subjects, recorded over both sensorimotor areas during imaginat ion of left and right hand movements. It is found that discrimination between both types of motor-imagery is possible using Linear discrimin ant analysis: but the time point for optimal classification is differe nt in each subject. For the estimation of the AAR parameters, the Leas t-mean-squares and the Recursive-least-squares algorithms are compared In both methods, the update coefficient plays a key role: it determin es the adaptation ratio as well as the estimation accuracy. A new meth od, based on minimizing the prediction error, is introduced for determ ining the update coefficient.