EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network
This work addresses interpretability and credibility issues in EEG analysis for clinical and neuroscience applications, representing an incremental improvement over existing methods.
The paper tackled the lack of interpretability and credibility in Graph Signal Processing for EEG analysis by proposing EEG-GMACN, which introduces an 'Inverse Graph Weight Module' and mutual attention mechanism to output interpretable electrode weights and improve classification, enhancing transparency for clinical use.
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.