Cerebral asymmetry representation learning-based deep subdomain adaptation network for electroencephalogram-based emotion recognition.

IF 2.3 4区 医学 Q3 BIOPHYSICS
Zhe Wang, Yongxiong Wang, Xin Wan, Yiheng Tang
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引用次数: 0

Abstract

Objective.Extracting discriminative spatial information from multiple electrodes is a crucial and challenging problem for electroencephalogram (EEG)-based emotion recognition. Additionally, the domain shift caused by the individual differences degrades the performance of cross-subject EEG classification.Approach.To deal with the above problems, we propose the cerebral asymmetry representation learning-based deep subdomain adaptation network (CARL-DSAN) to enhance cross-subject EEG-based emotion recognition. Specifically, the CARL module is inspired by the neuroscience findings that asymmetrical activations of the left and right brain hemispheres occur during cognitive and affective processes. In the CARL module, we introduce a novel two-step strategy for extracting discriminative features through intra-hemisphere spatial learning and asymmetry representation learning. Moreover, the transformer encoders within the CARL module can emphasize the contributive electrodes and electrode pairs. Subsequently, the DSAN module, known for its superior performance over global domain adaptation, is adopted to mitigate domain shift and further improve the cross-subject performance by aligning relevant subdomains that share the same class samples.Main Results.To validate the effectiveness of the CARL-DSAN, we conduct subject-independent experiments on the DEAP database, achieving accuracies of 68.67% and 67.11% for arousal and valence classification, respectively, and corresponding accuracies of 67.70% and 67.18% on the MAHNOB-HCI database.Significance.The results demonstrate that CARL-DSAN can achieve an outstanding cross-subject performance in both arousal and valence classification.

基于大脑不对称表征学习的深度子域适应网络,用于基于脑电图的情感识别。
目的:对于基于脑电图(EEG)的情绪识别而言,从多个电极中提取具有辨别力的空间信息是一个关键且极具挑战性的问题。此外,个体差异导致的域偏移会降低跨受试者脑电图分类的性能:针对上述问题,我们提出了基于大脑不对称表征学习的深度子域自适应网络(CARL-DSAN),以增强基于脑电图的跨主体情感识别能力。具体来说,CARL 模块的灵感来源于神经科学的研究成果,即在认知和情感过程中,左右大脑半球会出现不对称激活。在 CARL 模块中,我们引入了一种新颖的两步策略,通过大脑半球内空间学习和不对称表征学习来提取辨别特征。此外,CARL 模块中的变压器编码器可以强调有贡献的电极和电极对。随后,采用了以其优于全域自适应的性能而著称的 DSAN 模块来减轻域偏移,并通过调整共享相同类别样本的相关子域来进一步提高跨主体性能:为了验证 CARL-DSAN 的有效性,我们在 DEAP 数据库上进行了独立于主体的实验,结果表明唤醒和情绪分类的准确率分别为 68.67% 和 67.11%,在 MAHNOB-HCI 数据库上的相应准确率为 67.70% 和 67.18%:结果表明,CARL-DSAN 在唤醒和情绪分类方面都能实现出色的跨主体性能。
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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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