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Regularized common spatial patterns with subject-to-subject transfer of EEG signals
Minmin Cheng1, Zuhong Lu1, Haixian Wang1
1Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, 210096 Jiangsu China.
Cognitive Neurodynamics
|March 29, 2017
Summary
This study introduces a transfer learning approach to improve brain-computer interface (BCI) performance using electroencephalogram (EEG) signals. The method enhances classification accuracy with limited training data by incorporating inter-subject information.
Area of Science:
- Neuroscience
- Machine Learning
- Biomedical Engineering
Background:
- Brain-computer interfaces (BCI) utilize electroencephalogram (EEG) signals for control.
- Common Spatial Patterns (CSP) is a standard method for extracting EEG features.
- CSP performance degrades with limited training data from new users.
Purpose of the Study:
- To develop a novel approach for BCI systems that improves classification accuracy with minimal training data.
- To enhance the robustness of CSP in BCI applications for new users.
Main Methods:
- Proposed a regularized CSP method incorporating transfer learning.
- Integrated inter-subject EEG features by minimizing feature differences.
- Validated the approach on BCI competition datasets.
Main Results:
- The proposed transfer learning-regularized CSP significantly improved classification performance compared to conventional CSP.
- The method maintained or enhanced accuracy even with a small number of training samples.
- The approach demonstrated success across different datasets.
Conclusions:
- Transfer learning-based regularization of CSP is effective for improving BCI performance with limited user data.
- This method offers a robust solution for new BCI users.
- The findings have implications for developing more accessible and efficient BCI systems.
