MLDA: Multi-Loss Domain Adaptor for Cross-Session and Cross-Emotion EEG-Based Individual Identification
MLDA: Multi-Loss Domain Adaptor for Cross-Session and Cross-Emotion EEG-Based Individual Identification
复制标题
DOI：
10.1109/jbhi.2023.3315974
复制
发表时间：
2023-09
影响因子：
7.7
通讯作者：
Yifan Miao;Wanqing Jiang;Nuo Su;Jun Shan;Tianzi Jiang;Nianming Zuo
中科院分区：
文献类型：
--
作者：
Yifan Miao;Wanqing Jiang;Nuo Su;Jun Shan;Tianzi Jiang;Nianming Zuo
传统的个人身份识别方法，如人脸和指纹识别，存在个人信息泄露的风险。近年来，由于脑电信号的唯一性和隐私性以及脑电信号采集设备的普及，基于脑电信号的身份识别研究日益活跃。然而，大多数现有的工作使用来自单个会话或情绪的EEG信号，忽略了域之间的巨大差异。由于EEG信号不满足传统的深度学习假设，即训练集和测试集独立且同分布，因此训练模型很难对新会话或新情绪保持良好的分类性能。在这篇文章中，一个个体识别方法，称为多损失域适配器（MLDA），提出了处理不同的域引起的边缘和条件分布之间的差异。所提出的方法包括四个部分：a）特征提取器，其使用深度神经网络从EEG数据中提取深度特征; B）标签预测器，其使用全层网络来预测主题标签; c）边缘分布自适应，其使用最大平均差异（MMD）来减少边缘分布差异; d）关联域自适应，其适应条件分布差异。利用MLDA方法，通过减少时间和情绪的影响，解决了跨会话和跨情绪的基于EEG的个体识别问题。实验结果表明，该方法优于其他国家的最先进的方法。
Traditional individual identification methods, such as face and fingerprint recognition, carry the risk of personal information leakage. The uniqueness and privacy of electroencephalograms (EEG) and the popularization of EEG acquisition devices have intensified research on EEG-based individual identification in recent years. However, most existing work uses EEG signals from a single session or emotion, ignoring large differences between domains. As EEG signals do not satisfy the traditional deep learning assumption that training and test sets are independently and identically distributed, it is difficult for trained models to maintain good classification performance for new sessions or new emotions. In this article, an individual identification method, called Multi-Loss Domain Adaptor (MLDA), is proposed to deal with the differences between marginal and conditional distributions elicited by different domains. The proposed method consists of four parts: a) Feature extractor, which uses deep neural networks to extract deep features from EEG data; b) Label predictor, which uses full-layer networks to predict subject labels; c) Marginal distribution adaptation, which uses maximum mean discrepancy (MMD) to reduce marginal distribution differences; d) Associative domain adaptation, which adapts to conditional distribution differences. Using the MLDA method, the cross-session and cross-emotion EEG-based individual identification problem is addressed by reducing the influence of time and emotion. Experimental results confirmed that the method outperforms other state-of-the-art approaches.