Bi-hemisphere asymmetric attention network: recognizing emotion from EEG signals based on the transformer

EEG-based emotion recognition is not only an important branch in the field of affective computing, but is also an indispensable task for harmonious human-computer interaction. Recently, many deep learning emotion recognition algorithms have achieved good results, but most of them have been based on convolutional and recurrent neural networks, resulting in complex model design, poor modeling of long-distance dependency, and the inability to parallelize computations. Here, we proposed a novel bi-hemispheric asymmetric attention network (Bi-AAN) combining a transformer structure with the asymmetric property of the brain's emotional response. In this way, we modeled the difference of bi-hemispheric attention, and mined the long-term dependency between EEG sequences, which exacts more discriminative emotional representations. First, the differential entropy (DE) features of each frequency band were calculated using the DE-embedding block, and the spatial information between the electrode positions was extracted using positional encoding. Then, a bi-headed attention mechanism was employed to capture the intra-attention of frequency bands in each hemisphere and the attentional differences between the bi-hemispheric frequency bands. After carring out experiments both in DEAP and DREAMER datasets, we found that the proposed Bi-AAN achieved superior recognition performance as compared to state-of-the-art EEG emotion recognition methods.