文献类型：期刊文献

英文题名：Frame-Level Teacher-Student Learning With Data Privacy for EEG Emotion Recognition

作者：Gu, Tianhao[1];Wang, Zhe[1];Xu, Xinlei[1];Li, Dongdong[2];Yang, Hai[2];Du, Wenli[3]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2023

卷号：34

期号：12

起止页码：11021

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20221912092426);WOS:【SCI-EXPANDED(收录号:WOS:000791708300001)】；

基金：This work was supported in part by the Shanghai Science and Technology Program "Distributed and generative few-shot algorithm and theory research" under Grant 20511100600, in part by the Shanghai Science and Technology Program "Federated based cross-domain and crosstask incremental learning" under Grant 21511100800, in part by the Natural Science Foundation of China under Grant 62076094 and Grant 61806078, in part by the National Key Research and Development Project of Ministry of Science and Technology of China under Grant 2018AAA0101302, and in part by the National Science Foundation of China for Distinguished Young Scholars under Grant 61725301.

语种：英文

外文关键词：Electroencephalography; Emotion recognition; Feature extraction; Brain modeling; Robustness; Training; Logic gates; Data privacy (DP); electroencephalogram (EEG) emotion recognition; neural network; teacher-student network

摘要：Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention. This brief designs a novel frame-level teacher-student framework with data privacy (FLTSDP) for EEG emotion recognition. The framework first proposes a teacher-student network without prior professional information for automated filtering of useful frame-level features by a gated mechanism and extracting high-level features by using knowledge distillation to capture the results of EEG emotion recognition from a teacher network and student networks. Then, the results from subnetworks are integrated by using the novel decision module, which, motivated by the voting mechanism, adjusts the composition of feature vectors and improves the weight of accurate prediction to optimize the integration effect. During training, an innovative data privacy protection mechanism is applied for avoiding data sharing, where each student network only inherits weights from all trained networks and does not inherit the training dataset. Here, the framework can be repeatedly optimized and improved by only training the next student subnetwork on new EEG signals. Experimental results show that our framework improves the accuracy of EEG emotion recognition by more than 5% and gets state-of-the-art performance for EEG emotion recognition in the subject-independent mode.