What question did this study set out to answer?

To improve motor imagery EEG classification accuracy across subjects using meta-transfer learning techniques.

February 9, 2026

Cross-subject motor imagery EEG signal classification based on meta-transfer learning

Key Points

To improve motor imagery EEG classification accuracy across subjects using meta-transfer learning techniques.
Developed a novel CNN architecture called CMHA-Net optimized for motor imagery EEG classification.
Utilized depthwise separable convolution and multi-head attention mechanisms.
Implemented a Meta-SGD-based meta-transfer learning framework for training across different subjects.
Achieved classification accuracies of 81.61% and 88.15% on BCI-IV-2a and High Gamma datasets, respectively.
Outperformed existing models by 4-15% in accuracy, particularly in small-sample conditions.
Demonstrated the potential for enhanced clinical and real-world BCI applications.

Abstract

Motor imagery (MI) EEG classification, a core BCI task, faces challenges due to EEG's low signal-to-noise ratio and non-stationarity. Traditional supervised learning methods perform poorly in cross-subject and small-sample scenarios, limiting practical use. We propose CMHA-Net, a MI-EEG-optimized CNN integrating depthwise separable convolution, deep convolution and multi-head attention, combined with a Meta-SGD-based meta-transfer learning framework. Experiments on BCI-IV-2a and High Gamma datasets show 81.61% and 88.15% accuracy, outperforming existing models by 4-15% and excelling in small-sample cases, advancing clinical and real-world BCI applications.

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Discussion

Authors

Hui Li

Jiayi Liu

Jiayu Li

Journals

Computer Methods in Biomechanics & Biomedical Engineering

Actions

Institutions

Tiangong University

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Cross-subject motor imagery EEG signal classification based on meta-transfer learning

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

Cite this study

Also consider