title: Adaptive autoregressive modeling used for single-trial EEG classification.
abstract: An adaptive autoregressive (AAR) model is used for analyzing event-related EEG changes. Such an AAR model is applied to single EEG trials of three subjects, recorded over both sensorimotor areas during imagination of left and right hand movements. It is found that discrimination between both types of motor-imagery is possible using linear discriminant analysis, but the time point for optimal classification is different in each subject. For the estimation of the AAR parameters, the Least-mean-squares and the Recursive-least-squares algorithms are compared. In both methods, the update coefficient plays a key role: it determines the adaptation ratio as well as the estimation accuracy. A new method, based on minimizing the prediction error, is introduced for determining the update coefficient.
authors.forename: A
authors.initials: A
authors.lastname: Schlögl
authors.forename: D
authors.initials: D
authors.lastname: Flotzinger
authors.forename: G
authors.initials: G
authors.lastname: Pfurtscheller
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping; D004292:Dominance, Cerebral / Q000502:physiology; D004569:Electroencephalography / Q000145:classification* / Q000295:instrumentation; D019054:Evoked Potentials, Motor / Q000502:physiology; D006801:Humans; D008432:Mathematical Computing; D015233:Models, Statistical; D009044:Motor Cortex / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D013003:Somatosensory Cortex / Q000502:physiology