通过分层多尺度与自我认知机制提升脑机接口解码可靠性
Meta-cognitive Multi-scale Hierarchical Reasoning for Motor Imagery Decoding
- 分层多尺度处理重构脑电信号时序特征
- 引入自省置信度估计，平均准确率显著提升
- 适合脑机接口研究者与神经信号处理工程师
脑-机接口（BCI）旨在从非侵入性神经信号中解码运动意图以控制外部设备，但运动想象（MI）类脑电图（EEG）信号的噪声和个体差异限制了实际应用。本文提出一种分层式、具备元认知能力的四分类运动想象解码框架。设计多尺度分层信号处理模块，将主干网络特征重组为时序多尺度表示，并引入内省不确定性估计模块，对每周期赋予可靠性评分并引导迭代优化。在三个标准EEG主干模型（EEGNet、ShallowConvNet、DeepConvNet）上实现，基于BCI竞赛IV-2a数据集，在无受试者依赖设置下进行评估。所有主干模型下，所提组件均提升平均分类准确率并降低个体间方差，表明对受试者异质性和噪声试验具有更强鲁棒性。结果表明，将分层多尺度处理与内省置信估计结合，可显著增强基于运动想象的BCI系统可靠性。
原文摘要 · Abstract (English)
Brain-computer interface (BCI) aims to decode motor intent from noninvasive neural signals to enable control of external devices, but practical deployment remains limited by noise and variability in motor imagery (MI)-based electroencephalogram (EEG) signals. This work investigates a hierarchical and meta-cognitive decoding framework for four-class MI classification. We introduce a multi-scale hierarchical signal processing module that reorganizes backbone features into temporal multi-scale representations, together with an introspective uncertainty estimation module that assigns per-cycle reliability scores and guides iterative refinement. We instantiate this framework on three standard EEG backbones (EEGNet, ShallowConvNet, and DeepConvNet) and evaluate four-class MI decoding using the BCI Competition IV-2a dataset under a subject-independent setting. Across all backbones, the proposed components improve average classification accuracy and reduce inter-subject variance compared to the corresponding baselines, indicating increased robustness to subject heterogeneity and noisy trials. These results suggest that combining hierarchical multi-scale processing with introspective confidence estimation can enhance the reliability of MI-based BCI systems.
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