Transformer-based ensemble deep learning model for EEG-based emotion recognition

Emotion recognition is one of the most important research directions in the field of brain–computer interface(BCI).However,to conduct electroencephalogram(EEG)-based emotion recognition,there exist difficulties regarding EEG signal processing;moreover,the performance of classification models in this regard is restricted.To counter these issues,the 2022 World Robot Contest successfully held an affective BCI competition,thus promoting the innovation of EEG-based emotion recognition.In this paper,we propose the Transformer-based ensemble(TBEM)deep learning model.TBEM comprises two models:a pure convolutional neural network(CNN)model and a cascaded CNN-Transformer hybrid model.The proposed model won the abovementioned affective BCI competition’s final championship in the 2022 World Robot Contest,demonstrating the effectiveness of the proposed TBEM deep learning model for EEG-based emotion recognition.