Enhancing Neurophysiological Analysis and Emotion Recognition with EEG Channel Spatial Metadata through Graph Neural Networks

The proposed architecture leverages the recording device's metadata by encoding the initial position of the recording electrodes into a graph structure which is then processed by the corresponding neural network architecture. This approach has proven its merit in neurophysiological applications such as P300 pattern detection and emotion recognition. Our experiments highlight the crucial role of the coordinate graph within the algorithm, which drastically influences the performance and efficacy of the model. Additionally, the versatility of our model is showcased through its consistent performance across diverse tasks, confirming its potential as a robust framework for future EEG-based research. Further analysis reveals that the incorporation of graph-based data alongside advanced optimization strategies markedly enhances the model's ability to generalize, making it a valuable asset for the neuroscience community.