Local Discriminant Wavelet Packet Basis for Signal Classification in Brain Computer Interface

Abstract

A Brain-Computer Interface (BCI) is a system that provides direct communication between the brain of a person and the outside world. In the present work we use a BCI based on Event Related Potentials (ERP). The aim of this paper is to efficiently solve the classification problem consisting on labeling electroencephalogram records as target (with ERP) or non-target records (without ERP).We evaluate the performance of a BCI by using the Wavelet Packet Transform with the Local Discriminant Basis (LDB) method to find an orthogonal basis that maximizes the difference between the two classes involved. The performance of the LDB patterns and the temporal data (without post-processing) are analyzed with the Fisher Linear Classifier. It is shown that the bets results are obtained with LDB patterns calculated by Daubechies 4 as filter, Sum of Squares as discriminant function and the first 18 more discriminant basis vectors.