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.