  Electroencephalograph (EEG) emotion recognition is a significant task in the
brain-computer interface field. Although many deep learning methods are
proposed recently, it is still challenging to make full use of the information
contained in different domains of EEG signals. In this paper, we present a
novel method, called four-dimensional attention-based neural network (4D-aNN)
for EEG emotion recognition. First, raw EEG signals are transformed into 4D
spatial-spectral-temporal representations. Then, the proposed 4D-aNN adopts
spectral and spatial attention mechanisms to adaptively assign the weights of
different brain regions and frequency bands, and a convolutional neural network
(CNN) is utilized to deal with the spectral and spatial information of the 4D
representations. Moreover, a temporal attention mechanism is integrated into a
bidirectional Long Short-Term Memory (LSTM) to explore temporal dependencies of
the 4D representations. Our model achieves state-of-the-art performance on the
SEED dataset under intra-subject splitting. The experimental results have shown
the effectiveness of the attention mechanisms in different domains for EEG
emotion recognition.
