Multi-source adversarial adaptation with calibration for electroencephalogram-based classification of meditation and resting states.
10.7507/1001-5515.202504044
- Author:
Mingyu GOU
1
;
Haolong YIN
2
;
Tianzhen CHEN
3
;
Fei CHENG
3
;
Jiang DU
2
;
Baoliang LYU
2
;
Weilong ZHENG
2
Author Information
1. Paris Elite Institute of Technology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China.
2. School of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, P. R. China.
3. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, P. R. China.
- Publication Type:Journal Article
- Keywords:
Electroencephalogram;
Meditation;
Model integration;
Multi-source domain adaptation
- MeSH:
Humans;
Electroencephalography/methods*;
Meditation;
Calibration;
Neural Networks, Computer;
Brain/physiology*;
Rest/physiology*;
Deep Learning;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2025;42(4):668-677
- CountryChina
- Language:Chinese
-
Abstract:
Meditation aims to guide individuals into a state of deep calm and focused attention, and in recent years, it has shown promising potential in the field of medical treatment. Numerous studies have demonstrated that electroencephalogram (EEG) patterns change during meditation, suggesting the feasibility of using deep learning techniques to monitor meditation states. However, significant inter-subject differences in EEG signals poses challenges to the performance of such monitoring systems. To address this issue, this study proposed a novel model-calibrated multi-source adversarial adaptation network (CMAAN). The model first trained multiple domain-adversarial neural networks in a pairwise manner between various source-domain individuals and the target-domain individual. These networks were then integrated through a calibration process using a small amount of labeled data from the target domain to enhance performance. We evaluated the proposed model on an EEG dataset collected from 18 subjects undergoing methamphetamine rehabilitation. The model achieved a classification accuracy of 73.09%. Additionally, based on the learned model, we analyzed the key EEG frequency bands and brain regions involved in the meditation process. The proposed multi-source domain adaptation framework improves both the performance and robustness of EEG-based meditation monitoring and holds great promise for applications in biomedical informatics and clinical practice.
