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<p>Reliable motor decoding from single-channel EEG remains challenging due to low signal quality and strong inter-session variability. Traditional ERD/ERS features overlook transient high-frequency events that reflect key motor processes. This paper introduces a compact decoding framework that models gamma burst dynamics—including burst onset, duration, amplitude, and recurrence—as informative biomarkers of motor preparation and execution. Bursts are extracted using an analytic wavelet transform with adaptive thresholding, and combined with classical spectral features through a multi-stream adaptive deep learning model equipped with instance normalization and few-shot recalibration for session robustness. Experiments on the WAY-EEG-GAL dataset show that the proposed approach achieves competitive performance under a strict single-channel constraint and significantly improves cross-session accuracy by more than 10% over non-adaptive baselines. These findings highlight the functional relevance of gamma bursts and demonstrate the feasibility of practical, lightweight EEG-based motor BCIs.</p>

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Single-channel gamma burst dynamics and adaptive deep learning for robust EEG motor state decoding

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月18日

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  • Ravi Suppiah,
  • Noori Kim

摘要

Reliable motor decoding from single-channel EEG remains challenging due to low signal quality and strong inter-session variability. Traditional ERD/ERS features overlook transient high-frequency events that reflect key motor processes. This paper introduces a compact decoding framework that models gamma burst dynamics—including burst onset, duration, amplitude, and recurrence—as informative biomarkers of motor preparation and execution. Bursts are extracted using an analytic wavelet transform with adaptive thresholding, and combined with classical spectral features through a multi-stream adaptive deep learning model equipped with instance normalization and few-shot recalibration for session robustness. Experiments on the WAY-EEG-GAL dataset show that the proposed approach achieves competitive performance under a strict single-channel constraint and significantly improves cross-session accuracy by more than 10% over non-adaptive baselines. These findings highlight the functional relevance of gamma bursts and demonstrate the feasibility of practical, lightweight EEG-based motor BCIs.