@article{Si2023, 
author = {Xiaopeng Si and Dong Huang and Yulin Sun and Shudi Huang and He Huang and Dong Ming},
title = {Transformer-based ensemble deep learning model for EEG-based emotion recognition},
year = {2023},
journal = {Brain Science Advances},
volume = {9},
number = {3},
pages = {210-223},
keywords = {affective brain–computer interface, electroencephalogram, Transformer, deep learning, ensemble learning},
url = {https://www.sciopen.com/article/10.26599/BSA.2023.9050016},
doi = {10.26599/BSA.2023.9050016},
abstract = {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.}
}