App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 8

Content-adaptive wavelet features and discriminative hierarchical prototype alignment for cross-subject EEG decoding

Hu Xinxin1
Cao Miao1
Li Xiaolin3
Peng Bo2,3
Zhou Zhiyong2,3
Dai Yakang2,3
1. School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
2. Division of Life Sciences and Medicine，School of Biomedical Engineering (Suzhou)，University of Science and Technology of China，Suzhou Jiangsu 215163, China
3. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzho Jiangsu 215163, China
DOI: 10.19734/j.issn.1001-3695.2025.12.0515

Abstract

Cross-subject decoding of motor imagery EEG signals is affected by factors such as non-stationarity and inconsistent feature distributions, leading to insufficient model generalization. To address these issues, this algorithm proposes a cross-subject EEG decoding network (CAWF-DHPA network) based on content-adaptive wavelet features and discriminative hierarchical prototype alignment. The network architecture first constructs a content-adaptive wavelet feature extraction module (CAWFE module) , which enhances the stability of spatiotemporal representations by integrating multiscale temporal transient features with relevant frequency-domain information. Subsequently, a hierarchical prototype alignment module (DHPAN module) is employed to achieve collaborative correction of margins and conditional distributions, thereby obtaining feature representations that possess both domain invariance and class discriminability. Experiments on the BCI Competition IV 2a and OpenBMI datasets demonstrate that this method achieves average accuracy rates of 79.48% and 77.42%, respectively. The results indicate that the proposed method effectively mitigates inter-subject signal variations, thereby enhancing the stability and generalization potential of MI-EEG decoding in practical deployment.

Foundation Support

科技部-科技创新项目2030(2022ZD0208502)
国家自然科学基金资助项目(62471467)
中国博士后科学基金面上项目(188005063)
苏州市基础研究试点项目(SSD2023008)
苏州市重点实验室建设项目(SZS2024007)
苏州市关键核心技术项目(SYG2025124,SYG2025100)
医工所自主部署创新重点项目(E455380101)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.12.0515
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 8

Publish History

[2026-04-22] Accepted Paper

Cite This Article

胡欣欣, 曹秒, 李晓琳, 等. 基于内容自适应小波特征与判别式层次原型对齐的跨被试脑电解码网络 [J]. 计算机应用研究, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0515. (Hu Xinxin, Cao Miao, Li Xiaolin, et al. Content-adaptive wavelet features and discriminative hierarchical prototype alignment for cross-subject EEG decoding [J]. Application Research of Computers, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0515. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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