Research on dynamic domain adaptation selective ensemble algorithm for cross-subject EEG emotion classification

Electroencephalogram (EEG) has become an important medium for emotion recognition research due to its high temporal resolution and noninvasive nature. However, significant differences among subjects in skull structure, EEG physiological characteristics, and emotional perception lead to strong hetero...

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Published in:Journal of King Saud University. Computer and information sciences Vol. 37; no. 9; pp. 292 - 20
Main Authors: Qin, Yuanxian, Li, Danyang, Qin, Xue, Li, Chunlong, Li, Jialin, Yuan, Jiafan
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.11.2025
Springer Nature B.V
Springer
Subjects:
Accuracy
Adaptation
Algorithms
Classification
Computer Imaging
Computer Science
Cross-subject
Database Management
Domain adaptation
Effectiveness
Electroencephalogram (EEG)
Electroencephalography
Emotion recognition
Emotions
Feature selection
Heterogeneity
Machine Learning
Methods
Neighborhoods
Neural networks
Optimization
Original Paper
Pattern Recognition and Graphics
Physiology
Selective ensemble
Software Engineering/Programming and Operating Systems
Systems and Data Security
Temporal resolution
Theory of Computation
Vision
Domain adaptation
Emotion recognition
Electroencephalogram (EEG)
Selective ensemble
Cross-subject
ISSN:1319-1578, 2213-1248, 1319-1578
Online Access:Get full text
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Summary:Electroencephalogram (EEG) has become an important medium for emotion recognition research due to its high temporal resolution and noninvasive nature. However, significant differences among subjects in skull structure, EEG physiological characteristics, and emotional perception lead to strong heterogeneity in cross-subject EEG signals, severely limiting the generalization ability of emotion recognition models. To address this issue, this study proposes a cross-subject emotion recognition method. This method innovatively employs dynamic classifier selection to solve the cross-subject recognition problem. By constructing a pool of base classifiers and adaptively matching the optimal classifier for different samples, it mitigates the interference caused by inter-subject heterogeneity. Additionally, a neighborhood optimization strategy based on dynamic domain adaptation is proposed. This strategy maps samples from the test subject and the validation set into a common subspace to reduce distribution differences among samples. Subsequently, classifiers are dynamically selected from the base classifier pool for prediction based on this neighborhood. The effectiveness of the proposed method was evaluated on the public datasets FACED, SEED, and SEED-IV. Experimental results show that this method outperforms some of the current state-of-the-art cross-subject EEG emotion recognition algorithms, verifying its effectiveness and feasibility in emotion recognition tasks.
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ISSN:1319-1578
2213-1248
1319-1578
DOI:10.1007/s44443-025-00279-w