EEG Emotion Recognition Based on Federated Learning Framework

Emotion recognition based on the multi-channel electroencephalograph (EEG) is becoming increasingly attractive. However, the lack of large datasets and privacy concerns lead to models that often do not have enough data for training, limiting the research and application of Deep Learn (DL) methods in this direction. At present, the popular federated learning (FL) approach, which can collaborate with different clients to perform distributed machine learning without sending data to a central server, provides a solution to the above problem. In this paper, we extended the FL method to the field of emotion recognition based on EEG signals and evaluated its accuracy in the DEAP and SEED datasets, where the model accuracy reached 90.74% in our framework. We also divided the DEAP dataset into different clients. The accuracy of emotion recognition decreased by 29.31% compared to the FL method when the clients were trained using local data, which validates the necessity of the FL approach for emotion recognition tasks. In addition, we verified the impact of N-IID data on the accuracy of FL training. The experiment demonstrated that N-IID leads to a 14.89% decrease in accuracy compared to IID.