A parametric feature extraction and classification strategy for brain-computer interfacing

Abstract

Parametric modeling strategies are explored in conjunction with linear discriminant analysis for use in an electroencephalogram (EEG)-based brain-computer interface (BCI). A left/right self-paced typing exercise is analyzed by extending the usual autoregressive (AR) model for EEG feature extraction with an AR with exogenous input (ARX) model for combined filtering and feature extraction. The ensemble averaged Bereitschaftspotential (an event related potential preceding the onset of movement) forms the exogenous signal input to the ARX model. Based on trials with six subjects, the ARX case of modeling both the signal and noise was found to be considerably more effective than modeling the noise alone (common in BCI systems) with the AR method yielding a classification accuracy of 52.8 /spl plusmn/ 4.8% and the ARX method an accuracy of 79.1 /spl plusmn/ 3.9% across subjects. The results suggest a role for ARX-based feature extraction in BCIs based on evoked and event-related potentials.