Adaptive Classification of EEG Features with Sparse Feedback
D. R. Lowne, Iead A Rezek · 1999
Analysis of EEG for Brain Computer Interfacing (BCI) requires robust tools for discerning between brain states. In this paper, we explore an algorithm for extracting movement/non-movement data from EEG. We begin by projecting features from a dynamical system model of the EEG onto a nonlinear basis space. The basis function responses are then mapped via a logistic classifier onto a class-posterior decision space. The non-stationarity of the EEG signals is captured by parameterizing this mapping via a set of dynamically adaptive, time-dependent weights. We update these weights under a sequential Bayesian learning paradigm. Importantly, we aim such an adaptive classifier toward a system in which very few class labels are known such as is the case for self-paced BCI experiments.