ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Neural Technology
This article is part of the Research TopicClinically meaningful applications and evaluations of brain-computer interfacesView all articles
Transfer Learning for Subject-Independent Motor Imagery EEG Classification Using Convolutional Relational Networks
Provisionally accepted
- 1Nazarbayev University, Astana, Kazakhstan
- 2Astana IT University, Astana, Kazakhstan
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Motor imagery (MI) based electroencephalography (EEG) classification is central to brain–computer interface (BCI) research but practical deployment remains challenging due to poor generalization across subjects. Inter-individual variability in neural activity patterns significantly limits the development of subject-independent BCIs for healthcare and assistive technologies. To address this limitation, we present a transfer learning framework based on Convolutional Relational Networks (ConvoReleNet) designed to extract subject-invariant neural representations while minimizing the risk of catastrophic forgetting. The method integrates convolutional feature extraction, relational modeling, and lightweight recurrent processing, combined with pretraining on a diverse subject pool followed by conservative fine-tuning. Validation was conducted on two widely used benchmarks, BNCI IV-2a (four-class motor imagery) and BNCI IV-2b (binary motor imagery), to evaluate subject-independent classification performance. Results demonstrate clear improvements over training from scratch: accuracy on BNCI IV-2a increased from 72.22% (±20.49) to 79.44% (±11.09), while BNCI IV-2b improved from 75.10% (±17.17) to 83.85% (±10.30). The best-case performance reached 87.55% on BNCI IV-2a with Tanh activation and 83.85% on BNCI IV-2b with ELU activation, accompanied by reductions in inter-subject variance of 45.9% and 40.0%, respectively. These findings establish transfer learning as an effective strategy for subject-independent MI-EEG classification. By enhancing accuracy, reducing variability, and maintaining computational efficiency, the proposed framework strengthens the feasibility of robust and user-friendly BCIs for rehabilitation, clinical use, and assistive applications.
Keywords: Electroencephalography (EEG), Motor Imagery, brain–computer interface (BCI), Transfer Learning, Convolutional RelationalNetworks, Subject-independent classification, neural signal processing
Received: 24 Aug 2025; Accepted: 10 Nov 2025.
Copyright: © 2025 Otarbay, Kyzyrkanov and Abibullaev. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhenis Otarbay, zhenis.otarbay@nu.edu.kz
Abzal Kyzyrkanov, abzzall@gmail.com
Berdakh Abibullaev, berdakh.abibullaev@nu.edu.kz
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.