Enhanced Multi-Source Domain Adaptation for EEG Pattern Recognition Via Source Selection and Distance Function Optimization

Electroencephalography (EEG) based pattern recognition systems faces challenges in cross-subject generalization due to signal non-stationarity and inter-subject variability. Unsupervised domain adaptation (UDA) leverages labeled data from multiple source subjects to address this, yet existing methods often use a single distance metric for alignment and treat all sources equally, risking suboptimal or negative transfer. This paper presents an Enhanced Multi-Source Domain Adaptation (EMSDA) approach for EEG classification problems, enhancing the Multi-Source Marginal Distribution Adaptation (MSMDA) framework with multiple distance metrics (e.g., Maximum Mean Discrepancy (MMD), Correlation Alignment (CORAL)) for robust distribution alignment, a top-n close source selection strategy and a domain distance based strategy to weight sources. Evaluated on the Leipzig Mind-Brain-Body (LEMON) dataset, our method significantly outperforms baseline techniques in cross-subject classification, demonstrating adaptability to heterogeneous populations.