Multi-source Selective Graph Domain Adaptation Network for cross-subject EEG emotion recognition.

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Networks Pub Date : 2024-12-01 Epub Date: 2024-09-24 DOI:10.1016/j.neunet.2024.106742
Jing Wang, Xiaojun Ning, Wei Xu, Yunze Li, Ziyu Jia, Youfang Lin
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Abstract

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.

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用于跨主体脑电图情感识别的多源选择性图域自适应网络。
情感脑机接口是实现情感人机交互的重要组成部分。然而,客观存在的受试者个体差异极大地阻碍了脑电图(EEG)情感识别的应用。现有的方法仍然缺乏对脑电的主体不变性表征的完整提取,以及融合来自多个主体的有价值信息以促进目标主体的情感识别的能力。针对上述挑战,我们提出了一种多源选择性图域自适应网络(MSGDAN),它能更好地利用来自不同源主体的数据,对目标主体进行更稳健的情感识别。所提出的网络可提取和选择每个主体的特定个体信息,其中公共信息指的是来自多源主体的主体不变成分。此外,图域自适应网络通过动态图网络捕捉大脑的功能连接和区域状态,然后整合图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 的分类性能更优越。
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
期刊最新文献
Multi-source Selective Graph Domain Adaptation Network for cross-subject EEG emotion recognition. Spectral integrated neural networks (SINNs) for solving forward and inverse dynamic problems. Corrigendum to "Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning" [Neural Networks Volume 178, October (2024), 1-11/106414]]. MIU-Net: Advanced multi-scale feature extraction and imbalance mitigation for optic disc segmentation Recovering Permuted Sequential Features for effective Reinforcement Learning
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