IEEE Trans Neural Netw Learn Syst. 2023 Dec;34(12):11021-11028. doi: 10.1109/TNNLS.2022.3168935. Epub 2023 Nov 30.
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.
最近，脑电（EEG）情绪识别逐渐引起了人们的广泛关注。本文为 EEG 情绪识别设计了一种新颖的基于帧级的具有数据隐私保护的师生框架（FLTSDP）。该框架首先提出了一种无需先验专业信息的师生网络，通过门控机制自动过滤有用的帧级特征，并通过知识蒸馏提取高层特征，以捕获来自教师网络和学生网络的 EEG 情绪识别结果。然后，通过新颖的决策模块对子网的结果进行集成，该模块受投票机制的启发，调整特征向量的组成，并提高准确预测的权重，以优化集成效果。在训练过程中，应用了一种创新的数据隐私保护机制，以避免数据共享，其中每个学生网络仅从所有训练网络继承权重，而不继承训练数据集。在此，通过仅在新的 EEG 信号上训练下一个学生子网，框架可以重复进行优化和改进。实验结果表明，我们的框架提高了 EEG 情绪识别的准确性，超过 5%，并在独立于主体的 EEG 情绪识别模式下达到了最先进的性能。
