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Multi-View & Transfer Learning for Epilepsy Recognition Based on EEG Signals

Jiali Wang1, Bing Li2, Chengyu Qiu1, Xinyun Zhang1, Yuting Cheng1, Peihua Wang1, Ta Zhou3, Hong Ge2, Yuanpeng Zhang1,3,*, Jing Cai3,*

1 Department of Medical Informatics, Nantong University, Nantong, 226001, China
2 The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
3 Department of Health Technology and Informatics, Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen, 518057, China

* Corresponding Authors: Yuanpeng Zhang. Email: ; Jing Cai. Email:

Computers, Materials & Continua 2023, 75(3), 4843-4866.


Epilepsy is a central nervous system disorder in which brain activity becomes abnormal. Electroencephalogram (EEG) signals, as recordings of brain activity, have been widely used for epilepsy recognition. To study epileptic EEG signals and develop artificial intelligence (AI)-assist recognition, a multi-view transfer learning (MVTL-LSR) algorithm based on least squares regression is proposed in this study. Compared with most existing multi-view transfer learning algorithms, MVTL-LSR has two merits: (1) Since traditional transfer learning algorithms leverage knowledge from different sources, which poses a significant risk to data privacy. Therefore, we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance. (2) When utilizing multi-view data, we embed view weighting and manifold regularization into the transfer framework to measure the views’ strengths and weaknesses and improve generalization ability. In the experimental studies, 12 different simulated multi-view & transfer scenarios are constructed from epileptic EEG signals licensed and provided by the University of Bonn, Germany. Extensive experimental results show that MVTL-LSR outperforms baselines. The source code will be available on .


Cite This Article

J. Wang, B. Li, C. Qiu, X. Zhang, Y. Cheng et al., "Multi-view & transfer learning for epilepsy recognition based on eeg signals," Computers, Materials & Continua, vol. 75, no.3, pp. 4843–4866, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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