Yu Liang, Chenlong Zhang, Shan An, Zaitian Wang, Kaize Shi, Tianhao Peng, Yuqing Ma, Xiaoyang Xie, Jian He, Kun Zheng
Objective Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting...
May 3, 2024: Journal of Neural Engineering