Emotion recognition based on EEG (electroencephalography) has been widely used in human-computer interaction, distance education and health care. However, the conventional methods ignore the adjacent and symmetrical characteristics of EEG signals, which also contain salient information related to emotion. In this paper, a spatial folding ensemble network (SFE-Net) is presented for EEG feature extraction and emotion recognition. Firstly, for the undetected area between EEG electrodes, an improved Bicubic-EEG interpolation algorithm is developed for EEG channels information completion, which allows us to extract a wider range of adjacent space features. Then, motivated by the spatial symmetric mechanism of human brain, we fold the input EEG channels data with five different symmetrical strategies, which enable the proposed network to extract the information of space features of EEG signals more effectively. Finally, a 3DCNN-based spatial, temporal extraction, and a multi-voting strategy of ensemble learning are integrated to model a new neural network. With this network, the spatial features of different symmetric folding signals can be extracted simultaneously, which greatly improves the robustness and accuracy of emotion recognition. The experimental results on DEAP and SEED datasets show that the proposed algorithm has comparable performance in terms of recognition accuracy.
翻译:以 EEG 特征提取和情感识别为基础的空间折叠混合网络(SFE-Net) 。首先,对于 EEG 电极之间未探测的区域,为EEEG 频道完成信息开发了一个改进的Bicubic-EEEG 干涉算法,从而使我们能够提取更广泛的相邻空间特征。然后,在人类大脑空间对称机制的推动下,我们将EEEG 传输数据与五种不同的对称战略进行折叠,从而使拟议的网络能够更有效地提取EEEG信号空间特征的信息。最后,基于 3DCNNN的空间、时间提取和多表决式学习战略被整合到一个新的神经网络模型中。随着这个网络的建立,不同对称信号的空间特征可以产生更广泛的相邻空间特征。然后,我们借助人类大脑空间对称机制,将输入 EEG 传输数据与五种不同的对称战略相连接数据进行折叠,从而使得拟议的网络能够同时获取可靠的性能识别和SEEA 的精确度数据,从而大大地显示SEA 的精确度。