Beijing Key Laboratory of Traffic Data Analysis and Mining, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
Beijing Key Laboratory of Traffic Data Analysis and Mining, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China; Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Neural Netw. 2024 Dec;180:106742. doi: 10.1016/j.neunet.2024.106742. Epub 2024 Sep 24.
Affective brain-computer interface is an important part of realizing emotional human-computer interaction. However, existing objective individual differences among subjects significantly hinder the application of electroencephalography (EEG) emotion recognition. Existing methods still lack the complete extraction of subject-invariant representations for EEG and the ability to fuse valuable information from multiple subjects to facilitate the emotion recognition of the target subject. To address the above challenges, we propose a Multi-source Selective Graph Domain Adaptation Network (MSGDAN), which can better utilize data from different source subjects and perform more robust emotion recognition on the target subject. The proposed network extracts and selects the individual information specific to each subject, where public information refers to subject-invariant components from multi-source subjects. Moreover, the graph domain adaptation network captures both functional connectivity and regional states of the brain via a dynamic graph network and then integrates graph domain adaptation to ensure the invariance of both functional connectivity and regional states. To evaluate our method, we conduct cross-subject emotion recognition experiments on the SEED, SEED-IV, and DEAP datasets. The results demonstrate that the MSGDAN has superior classification performance.
情感脑机接口是实现情感人机交互的重要组成部分。然而，现有受试者之间的客观个体差异显著阻碍了脑电图（EEG）情绪识别的应用。现有的方法仍然缺乏对 EEG 中不变主体表示的完整提取，以及融合来自多个主体的有价值信息的能力，以促进目标主体的情绪识别。为了解决上述挑战，我们提出了多源选择性图域自适应网络（MSGDAN），它可以更好地利用来自不同源主体的数据，并对目标主体进行更稳健的情绪识别。所提出的网络提取并选择每个主体特有的个体信息，其中公共信息是指来自多源主体的主体不变成分。此外，图形域自适应网络通过动态图形网络捕获大脑的功能连接和区域状态，然后集成图形域自适应以确保功能连接和区域状态的不变性。为了评估我们的方法，我们在 SEED、SEED-IV 和 DEAP 数据集上进行了跨主体情绪识别实验。结果表明，MSGDAN 具有优越的分类性能。
