Meta-Learning for BCI: A Promising New Direction
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Conference Proceeding
- Citation:
- 2025 International Conference on Fuzzy Theory and Its Applications (iFUZZY), 2025, 00, pp. 1-5
- Issue Date:
- 2025-11-08
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Despite impressive results in controlled settings, EEG-based Brain-Computer Interface (BCI) systems often falter in real-world scenarios due to challenges such as low signal-to-noise ratios (SNR), limited subject/trial datasets, poor cross-subject generalization, lengthy calibration, and lack of robustness outside the laboratory. Meta-learning (MeL) offers a compelling solution by enabling models to “learn how to learn,” with support-query paradigms, fast adaptation, and task-aware inference. We examine two representative implementations - Model-Agnostic-Meta-Learning for EEG (MAML-EEG) and Adaptive Bayesian Meta-Learning (ABML) - demonstrating strong performance on BCI Competition IV datasets, outperforming established baselines without subject-dependent calibration. We conclude by summarizing core contributions, outlining future research paths, and highlighting the potential of MeL to unify disparate BCI challenges into an integrated, scalable framework.
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