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arXiv Machine Learning By Jinzhou Wu, Baoping Tang, Qikang Li, Yi Wang, Cheng Li, Shujian Yu

Brain foundation model-guided source-selective domain adaptation for cross-subject EEG decoding

Read the original on arXiv Machine Learning →

The paper introduces BFM-MSDA, a brain foundation model-guided multi-source domain adaptation framework for cross-subject motor‑imagery EEG decoding. It first uses pretrained brain foundation model representations to estimate source–target compatibility and select relevant source subjects. A relevance-weighted dual alignment strategy then aligns marginal and conditional feature distributions, achieving higher accuracies on two public MI‑EEG benchmarks and demonstrating the benefits of informed source selection.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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