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.
翻译:虽然最近提出了许多深层学习方法,但充分利用EEG信号不同领域所含信息仍具有挑战性。在本文中,我们介绍了一种新颖的方法,称为四维关注神经网络(4D-anN),用于EEG情感识别。首先,原始EEEG信号转换成4D空间光谱时空表达方式。然后,拟议的4D-aNN采用光谱和空间关注机制,以适应性地分配不同脑区域和频带的重量,并使用动态神经网络(CNN)处理4D表示的光谱和空间信息。此外,将时间关注机制纳入双向短期内存(LSTM),以探索4D表示方式的时间依赖性。我们的模型在内部分裂下取得了SEECD数据集的最新性表现。实验结果显示,不同领域对EEEG情感识别的注意机制是有效的。