Multiclass Common Spatial Pattern for EEG based Brain Computer Interface with Adaptive Learning Classifier

TL;DR

This paper introduces a novel multiclass CSP-based EEG classification method that incorporates artifact removal and an adaptive neuro-fuzzy classifier, significantly improving accuracy in BCI applications.

Contribution

It extends the SRIT2NFIS classifier to multiclass EEG data using JAD and integrates artifact removal for better feature extraction, addressing non-stationarity and outliers.

Findings

Achieved higher classification accuracy on BCI dataset

Effectively handled non-stationarity and artifacts in EEG data

Outperformed existing multiclass BCI classification methods

Abstract

In Brain Computer Interface (BCI), data generated from Electroencephalogram (EEG) is non-stationary with low signal to noise ratio and contaminated with artifacts. Common Spatial Pattern (CSP) algorithm has been proved to be effective in BCI for extracting features in motor imagery tasks, but it is prone to overfitting. Many algorithms have been devised to regularize CSP for two class problem, however they have not been effective when applied to multiclass CSP. Outliers present in data affect extracted CSP features and reduces performance of the system. In addition to this non-stationarity present in the features extracted from the CSP present a challenge in classification. We propose a method to identify and remove artifact present in the data during pre-processing stage, this helps in calculating eigenvectors which in turn generates better CSP features. To handle the non-stationarity, Self-Regulated Interval Type-2 Neuro-Fuzzy Inference System (SRIT2NFIS) was proposed in the literature for two class EEG classification problem. This paper extends the SRIT2NFIS to multiclass using Joint Approximate Diagonalization (JAD). The results on standard data set from BCI competition IV shows significant increase in the accuracies from the current state of the art methods for multiclass classification.