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.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that has gradually been applied in emotion recognition research due to its advantages of high spatial resolution, real time, and convenience. However, the current research on emotion recognition based on fNIRS is mainly limited to within-subject, and there is a lack of related work on emotion recognition across subjects. Therefore, in this paper, we designed an emotion evoking experiment with videos as stimuli and constructed the fNIRS emotion recognition database. On this basis, deep learning technology was introduced for the first time, and a dual-branch joint network (DBJNet) was constructed, creating the ability to generalize the model to new participants. The decoding performance obtained by the proposed model shows that fNIRS can effectively distinguish positive versus neutral versus negative emotions (accuracy is 74.8%, F1 score is 72.9%), and the decoding performance on the 2-category emotion recognition task of distinguishing positive versus neutral (accuracy is 89.5%, F1 score is 88.3%), negative versus neutral (accuracy is 91.7%, F1 score is 91.1%) proved fNIRS has a powerful ability to decode emotions. Furthermore, the results of the ablation study of the model structure demonstrate that the joint convolutional neural network branch and the statistical branch achieve the highest decoding performance. The work in this paper is expected to facilitate the development of fNIRS affective brain–computer interface.