A Vector-based EEG Signal Feature Extraction Technique for BCI Applications

Abstract

Brain Computer Interface (BCI) systems offer the ability to effect actuations in the users environment, bypassing the neuro-muscular pathway. The optimal functioning of BCI systems is predicated on two important aspects of the analysis pipeline - informative feature extraction and accurate classification. We propose a simple yet distinct approach to perform the former using a vector-based treatment of signal data and covariance matrices. Our results show a comparable level of performance to certain variants of CSP algorithm. We also present the optimal classifier parameters obtained after parameter-tuning of certain standard classifier models over the BCI Competition III, data-set IVa.