Enhanced motor imagery-based EEG classification using a discriminative graph Fourier subspace

TL;DR

This paper introduces a graph signal processing approach for EEG-based motor imagery classification that leverages a discriminative subspace to improve accuracy, outperforming existing methods on a standard dataset.

Contribution

The paper proposes a novel GSP-based method using simultaneous diagonalization to extract discriminative features for motor imagery EEG classification.

Findings

Outperforms two state-of-the-art methods on BCI Competition III Dataset IVa

Provides a discriminative subspace for feature extraction

Enhances classification accuracy in motor imagery tasks

Abstract

Dealing with irregular domains, graph signal processing (GSP) has attracted much attention especially in brain imaging analysis. Motor imagery tasks are extensively utilized in brain-computer interface (BCI) systems that perform classification using features extracted from Electroencephalogram signals. In this paper, a GSP-based approach is presented for two-class motor imagery tasks classification. The proposed method exploits simultaneous diagonalization of two matrices that quantify the covariance structure of graph spectral representation of data from each class, providing a discriminative subspace where distinctive features are extracted from the data. The performance of the proposed method was evaluated on Dataset IVa from BCI Competition III. Experimental results show that the proposed method outperforms two state-of-the-art alternative methods.