Motor-imagery classification model for brain-computer interface: a sparse group filter bank representation model

TL;DR

This paper introduces a sparse group filter bank model for motor imagery EEG classification that enhances BCI performance by robust feature extraction and multi-task learning, outperforming existing methods on public datasets.

Contribution

The paper proposes a novel sparse group filter bank model (SGFB) that addresses small sample issues and improves feature robustness in motor imagery BCI classification.

Findings

The SGFB model achieves competitive classification accuracy.

Joint sparse optimization enhances feature robustness.

The method outperforms existing approaches on public EEG datasets.

Abstract

Background: Common spatial pattern (CSP) has been widely used for feature extraction in the case of motor imagery (MI) electroencephalogram (EEG) recordings and in MI classification of brain-computer interface (BCI) applications. BCI usually requires relatively long EEG data for reliable classifier training. More specifically, before using general spatial patterns for feature extraction, a training dictionary from two different classes is used to construct a compound dictionary matrix, and the representation of the test samples in the filter band is estimated as a linear combination of the columns in the dictionary matrix. New method: To alleviate the problem of sparse small sample (SS) between frequency bands. We propose a novel sparse group filter bank model (SGFB) for motor imagery in BCI system. Results: We perform a task by representing residuals based on the categories corresponding to the non-zero correlation coefficients. Besides, we also perform joint sparse optimization with constrained filter bands in three different time windows to extract robust CSP features in a multi-task learning framework. To verify the effectiveness of our model, we conduct an experiment on the public EEG dataset of BCI competition to compare it with other competitive methods. Comparison with existing methods: Decent classification performance for different subbands confirms that our algorithm is a promising candidate for improving MI-based BCI performance.