SFE-Net：基于对称空间特征提取的EEG情感识别
SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction
摘要：
基于脑电信号的情感识别在人机交互、远程教育和医疗保健等领域有着广泛的应用。然而，传统的方法忽略了相邻和对称的特征，脑电信号，其中也包含了显着的信息有关的情绪。本文提出了一种空间折叠集成网络（SFE-Net）用于脑电特征提取和情感识别。首先，针对脑电电极间的未检测区域，提出了一种改进的Bicubic-EEG插值算法，用于脑电通道信息的补齐，使我们能够提取更大范围的相邻空间特征。然后，根据人脑空间对称性的特点，采用5种不同的对称策略对输入脑电信号通道数据进行折叠，使网络能够更有效地提取脑电信号的空间特征信息。最后，一个基于3DCNN的空间，时间提取，集成学习的多投票策略集成到一个新的神经网络模型。利用该网络，可以同时提取不同对称折叠信号的空间特征，大大提高了情感识别的鲁棒性和准确性。在DEAP和SEED数据集上的实验结果表明，该算法在识别精度方面具有相当的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
