Adaptive Classification of EEG Features with Sparse Feedback
نویسندگان
چکیده
An important problem in the field of online EEG analysis is that of adaptive classification. The non-stationarity nature of EEG renders simple linear systems ineffective. Moreover, often class labels are available only occasionally. We present an algorithm for adaptive nonlinear two-class distinction which extends the non-stationary logistic regression [2] to an environment in which feedback is only sparsely available.
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تاریخ انتشار 2006