EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network

TL;DR

This paper introduces EEG-GMACN, a novel neural network that enhances interpretability and credibility in EEG analysis by using graph-based attention mechanisms and an inverse graph weight module.

Contribution

It proposes an interpretable EEG classification model with a mutual attention mechanism and credibility calibration, improving transparency and clinical applicability.

Findings

Enhanced interpretability of electrode importance.

Improved accuracy in EEG classification.

Credibility calibration for uncertainty assessment.

Abstract

Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and developing brain-computer interface. Graph Signal Processing (GSP) has emerged as a promising method for EEG spatial-temporal analysis, by further considering the topological relationships between electrodes. However, existing GSP studies lack interpretability of electrode importance and the credibility of prediction confidence. This work proposes an EEG Graph Mutual Attention Convolutional Network (EEG-GMACN), by introducing an 'Inverse Graph Weight Module' to output interpretable electrode graph weights, enhancing the clinical credibility and interpretability of EEG classification results. Additionally, we incorporate a mutual attention mechanism module into the model to improve its capability to distinguish critical electrodes and introduce credibility calibration to assess the uncertainty of prediction results. This study enhances the transparency and effectiveness of EEG analysis, paving the way for its widespread use in clinical and neuroscience research.