Domain-aware domain-class adaptation network for motor execution to motor imagery EEG classification.

Overview

1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
2. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, China
3. The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
4. State Industry-Education Integration Center for Medical Innovations, Xi’an Jiaotong University, Xi’an, China

Abstract

Introduction: Motor imagery (MI) is one of the most widely used paradigms in electroencephalogram (EEG)-based brain–computer interfaces (BCIs). In recent years, deep learning and transfer learning techniques have been increasingly adopted to further improve MI-EEG decoding performance, thereby facilitating the practical deployment of BCIs. In transfer learning, the similarity between the source and target domains is a critical factor influencing its effectiveness. Given the analogous cortical activation patterns observed in MI and motor execution (ME) tasks, cross-task transfer learning from ME to MI presents a promising yet underexplored direction.

Methods: To tackle the underexplored problem of cross-task transfer learning from ME to MI, we propose a domain-aware domain–class adaptation network (DDCA Net), which consists of a domain-shared feature extractor, two classifiers, and two domain-specific feature re-weighting blocks. Domain-level alignment is achieved by minimizing the maximum mean discrepancy between source and target feature distributions, while domain-specific feature re-weighting preserves discriminative characteristics unique to each task. In addition, a bi-classifier adversarial learning framework is employed to encourage consistency of decision boundaries across domains, thereby enabling implicit class-level alignment.

Results: Extensive experiments were conducted on a public dataset with over 100 subjects under varying proportions of target-domain training samples. When 80% of target-domain samples are used for training, the proposed DDCA Net significantly outperforms the within-task baseline, achieving a 7.71% improvement in classification accuracy and converting approximately 80% of previously BCI-illiterate subjects into BCI-literate users.

Discussion: To the best of our knowledge, this is the first work to verify the feasibility of applying domain adaptation for cross-task transfer learning in MI-EEG classification. The findings of this study provide new insights for integrating ME and MI in advanced BCIs.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• physionet.org/content/eegmmidb — at PhysioNet; found in “Data availability statement”

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found at: https://physionet.org/content/eegmmidb/1.0.0/.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 6 keywords, 39 references, 1 integrity notice.

Cite

This paper

Wang, J., Xu, G., Du, C., Li, Z., Li, H., Chen, S., Han, C., & Zhang, S. (2026). Domain-aware domain-class adaptation network for motor execution to motor imagery EEG classification. Frontiers in neuroscience, 20, 1851006. https://doi.org/10.3389/fnins.2026.1851006

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