Reinforcement Learning Decoding Method of Multi-User EEG Shared Information Based on Mutual Information Mechanism

The multi-user motor imagery brain-computer interface (BCI) is a new approach that uses information from multiple users to improve decision-making and social interaction. Although researchers have shown interest in this field, the current decoding methods are limited to basic approaches like linear averaging or feature integration. They ignored accurately assessing the coupling relationship features, which results in incomplete extraction of multi-source information. To overcome these limitations, we propose a new reinforcement learning electroencephalography (EEG) decoding method based on mutual information mechanisms. Our method enhances the extraction of multi-source common information and uses a dynamic feedback model for inter-brain mutual information reward and punishment mechanisms in the reinforcement learning channel selection module. We feed the single-brain and inter-brain signals after channel selection into deep neural networks, which automatically extract coupled features. Finally, based on the attention indices calculated from EEG signals at prefrontal electrode positions, the output is obtained by voting. Our experimental results show that the average accuracy of dual-brain recognition is improved by 16% compared to single-brain mode. Furthermore, ablation experiments demonstrate that the reinforcement learning module and attention voting module enhance accuracy by 14.5% and 15.7%, respectively.