Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism

TL;DR

This paper introduces an unsupervised self-attention based method to automatically identify salient segments in MI-EEG signals, improving BCI system performance and reducing processing complexity.

Contribution

It presents a novel unsupervised approach using self-attention for MI-EEG saliency detection, applicable as a preprocessing step in BCI systems.

Findings

Enhanced classification accuracy with the proposed method

Effective pruning of MI-EEG signals

Significant performance improvement in CSP algorithm

Abstract

Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG) signals can enhance the performance of the brain-computer interface (BCI) system and reduce the computational burden required for processing lengthy MI-EEG signals. In this paper, we propose an unsupervised method based on the self-attention mechanism to detect the salient intervals of MI-EEG signals automatically. Our suggested method can be used as a preprocessing step within any BCI algorithm to enhance its performance. The effectiveness of the suggested method is evaluated on the most widely used BCI algorithm, the common spatial pattern (CSP) algorithm, using dataset 2a from BCI competition IV. The results indicate that the proposed method can effectively prune MI-EEG signals and significantly enhance the performance of the CSP algorithm in terms of classification accuracy.