Manifold-based multi-branch transfer learning for MI-EEG decoding

Haoyu Li, Jianpeng An, Rongshun Juan, Tina P. Benko, Matjaž Perc, Weidong Dang and Zhongke Gao

Chaos, Solitons & Fractals, 2025, vol. 196, issue C

Abstract: We present a novel decoding approach for motor imagery electroencephalograms (MI-EEG) in Brain-Computer Interface (BCI) systems, aiming to advance neurorehabilitation and human–computer interaction. However, due to the non-stationary properties and low signal-to-noise ratio of MI-EEG, as well as significant individual variability, achieving accurate cross-subject decoding remains a challenge for real-world applications. To address this, we propose a manifold-based data processing method combined with a multi-branch network and enhanced by transfer learning to improve cross-subject performance. First, we identify the frequency bands relevant to motor imagery tasks and align multi-band data on the Symmetric Positive Definite (SPD) manifold using the Log-Euclidean Metric (LEM), including both full-band data and motor imagery-specific frequency bands. We then reconstruct these data from the manifold using a low-rank representation (LRP). Finally, we use a multi-branch network to extract and fuse deep features from the various frequency bands. We validated our approach on the BCI Competition IV-2a dataset and our JS-MI dataset, demonstrating that our method excels in cross-subject MI-EEG decoding tasks, with average classification accuracies of 74.55% and 71.94%, respectively. Our findings highlight the potential of this approach to improve BCI applications and facilitate more effective neurorehabilitation and human–computer interaction through enhanced MI-EEG decoding.

Keywords: Brain-computer interfaces; Electroencephalogram; Motor imagery; Manifold learning; Deep learning; Transfer learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:196:y:2025:i:c:s096007792500339x

DOI: 10.1016/j.chaos.2025.116326

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