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Transfer learning for EEG-based BCIs: a comparative evaluation and optimization of data alignment methods
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DOI：10.3389/fnsys.2026.1840121.png)
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Abstract
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BackgroundThis paper addresses a critical challenge in developing practical EEG-based brain-computer interfaces (BCIs): enhancing cross-subject generalization by mitigating individual differences in brain signals. How can we effectively leverage data from existing subjects to improve performance for a new user with minimal subject-specific calibration?MethodsWe systematically compare and optimize three prominent data alignment techniques; Riemannian Procrustes Analysis (RPA); Euclidean Alignment (EA); and Correlation Alignment (CORAL); designed to transform EEG data from multiple source subjects and a target subject into a common representation space; mitigating variability.EvaluationWe employed leave-one-subject-out cross-validation (LOSO-CV) framework on EEG-based attention decoding data to empirically evaluate the effectiveness of each alignment method compared to a baseline condition with no alignment. Key parameters; specifically the regularization parameter α for EA; were optimized to maximize cross-subject transfer performance.ResultsThe study demonstrates that alignment methods improve classification accuracy compared to the baseline. Notably; EA evaluated at α = 100 the scaling value at which the largest fraction of subjects attained their best accuracy in our parameter sweep yielded the largest mean improvement; increasing classification accuracy by 3.44% over the no alignment baseline (paired t(17)≈2.48; p≈0.024; Cohen's dz≈0.59; 95% confidence interval for the mean improvement [0.52%; 6.36%]). Because this α value was identified from the same sweep that produced the per-subject accuracies; this estimate together with the per-subject “best-parameter” results should be interpreted as an oracle sensitivity-analysis upper bound on subject-specific tuning rather than as a leakage-free LOSO estimate. While optimized EA showed the best mean performance; the analysis also demonstrated subject-specific differences in the most ideal alignment strategy.ConclusionThis comparison framework quantifies the benefits of different alignment approaches and highlights the valuable contribution of parameter optimization; particularly for EA.SignificanceThese results indicate the potential of optimized alignment techniques; EA in particular; to significantly enhance cross-subject transfer learning in EEG-based BCIs. This has practical ramifications for methodology selection and tuning; and maps a path toward more robust and generalizable BCI systems requiring less subject-specific calibration for real-world applications.
Keywords:
EEG
transfer learning
brain-computer interface
coral
cross-subject generalization
data alignment
Euclidean Alignment
Riemannian Procrustes Analysis
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