细粒度脑电成分弱监督标注用于伪迹衰减的概念验证研究

中文摘要 AI 辅助

脑电图（EEG）极易受到肌电图（EMG）伪迹的影响，这些伪迹的时间异质性以及与神经活动在空间和频谱上的重叠，可能导致盲源分离后仍存在混合源。现有的伪迹去除方法进一步受到稀缺的可靠成分级真实标注的限制：专家标注成本高昂且具有主观性，而目前尚无成熟方法能为多通道头皮脑电中的EMG污染提供基于仿真的真实标注。为解决这些局限性，我们提出了一种将频率感知的高维表示与多实例学习相结合的框架。该表示将分离后的成分展开为频率分辨的亚成分，从而构建一个混合神经与肌肉活动更易分离的空间，同时弱监督学习范式使得仅凭时段级标签即可学习各亚成分的伪迹可能性评分，无需更细粒度的真实标注。由此得到的亚成分分类器支持细粒度EMG伪迹检测和评分引导的衰减。在留出受试者上的实验表明，该框架能学习到信息丰富的亚成分评分，并能最显著地减少与伪迹相关的频谱偏差（尤其针对下颌紧张），对抬眉的效果中等，对皱眉的效果有限。

英文摘要

Electroencephalography (EEG) is highly susceptible to electromyographic (EMG) artifacts, whose temporal heterogeneity and spatial-spectral overlap with neural activity can leave mixed sources after blind source separation. Existing artifact-removal methods are further limited by scarce reliable component-level ground truth: expert annotations are costly and subjective, while no established method provides realistic simulation-based ground truth for EMG contamination in multichannel scalp EEG. To address these limitations, we propose a framework combining a frequency-aware high-dimensional representation with Multi-Instance Learning. The representation unfolds separated components into frequency-resolved intra-components, creating a space in which mixed neural and muscular activity becomes more separable, while the weakly supervised learning formulation enables artifact-likelihood scores for individual intra-components to be learned from epoch-level labels without finer-grained ground truth. The resulting intra-component classifier supports fine-grained EMG artifact detection and score-guided attenuation. Experiments on held-out subjects show that the framework learns informative intra-component scores and reduces artifact-related spectral deviations most clearly for jaw tension, with moderate effects for raising eyebrows and limited effects for frowning.

发表机构

• Institute for Applied Computer Science, University of the Bundeswehr Munich（慕尼黑联邦国防军大学应用计算机科学研究所）
• brainboost GmbH（brainboost 有限公司）
• Graduate School of Engineering Science, The University of Osaka（大阪大学工学研究科）
• Graduate School of Frontier Sciences, The University of Tokyo（东京大学新领域创成科学研究科）

机构由 AI 辅助整理，请以论文原文为准。