Noise-Robust and Interpretable EEG Classification of Hyperactive Disorders Using Graph Convolutional Network

The analysis of EEG data has been widely utilized due to its non-invasive nature. However, the typically noisy characteristics of EEG signals necessitate artifact removal processes. These processes can involve excessive computation or additional devices, significantly compromising the high usability inherent to non-invasive techniques. Although deep learning was expected to overcome these limitations, it still requires artifact removal and thus becomes highly inefficient. Moreover, it suffers from a lack of interpretability due to its black-box nature. To address these challenges, we propose and validate a framework utilizing Graph Convolutional Network (GCN) and PGExplainer in this study. Specifically, to demonstrate the noise-robustness of our model, we applied it to classify hyperactive disorders such as Tic disorder and Attention deficit hyperactivity disorder (ADHD), which generate excessive noise due to movement. Using EEG data from patients with tics and ADHD, we demonstrated that our model predicts at least 20% higher accuracy than any other neural network models, while the analysis with PGExplainer showed that the same channels were identified as important whether or not noise was removed, indicating noise-robust interpretability of the model. Additionally, this method allowed us to increase computational efficiency, thus identifying a maximally efficient model, i.e., a model that balances accuracy with minimal complexity. The proposed framework is not limited to our case of hyperactive disorder, but is expandable to more general cases. This work demonstrates that GCN-based classification can provide a noise-robust and interpretable solution for EEG analysis.