Parametric models and spectral analysis for classification in brain-computer interfaces

Abstract

Parametric modelling strategies and spectral analysis are explored in conjunction with linear discriminant analysis to facilitate an EEG based direct-brain interface for use by disabled people. A self-paced typing exercise is analysed by employing for feature extraction, respectively, an autoregressive model, an autoregressive with exogenous input model, and a time-frequency decomposition of the data. Modelling both the signal and noise is found to be more, effective than modelling the noise alone with the former yielding an accuracy of 70.7% and the latter an accuracy of 57.4%. Experiments, using the raw samples of a short-time power spectral density estimate of each trial as features, yielded an accuracy of 62.5%.