Interactive multi-agent convolutional broad learning system for EEG emotion recognition

Electroencephalogram (EEG) emotion recognition is gaining significance in intelligent human-computer interaction. Multi-agent learning can capture more complete and reliable features, compensating for the lack of a single agent's knowledge. However, previous EEG emotion recognition only focused on a single agent. Therefore, to extract effective features from high-dimensional EEG data, a novel interactive multi-agent (IMA) learning framework is proposed, and introduced into convolutional broad learning system (CNNBLS), then the interactive multi-agent convolutional broad learning system (IMA-CNNBLS) is proposed for EEG emotion recognition. It can effectively model high-dimensional EEG data, automatically extract EEG features related to emotions through convolutional neural network (CNN), and then extend the above features to a vast space to quickly extract generalized features through broad learning system (BLS), consider a CNNBLS as an agent, and multi-agent interact in the IMA part. As the theoretical basis of IMA-CNNBLS, the consistency is mathematically proved through the stationary distribution theory of Markov processes. To demonstrate the superiority of our proposed model, sufficient experiments are conducted on the DEAP, DREAMER and SEED datasets. The experimental results show the model can effectively improve the EEG emotion recognition accuracy, the best recognition performance is achieved on all datasets. In addition, our proposed model also shows the multi-agent interaction significantly affects EEG emotion recognition.