Abstract
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.
Keywords
Publication details
- Journal
- Not available
- Open access
- Green open access
Cite this article
APA 7
Wang-Nöth, L., Huang, H., Heiler, P., Wu, S., Zhang, L., & Mayer, H. (2026). A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation. https://omanscience.com/en/articles/a-proof-of-concept-study-of-weakly-supervised-labeling-of-fine-grained-eeg-components-for-artifact-attenuation
MLA 9
Wang-Nöth, Lu, et al. "A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation." https://omanscience.com/en/articles/a-proof-of-concept-study-of-weakly-supervised-labeling-of-fine-grained-eeg-components-for-artifact-attenuation.
Chicago (author–date)
Wang-Nöth, Lu, Hai Huang, Philipp Heiler, Shuqiong Wu, Liyun Zhang, and Helmut Mayer. 2026. "A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation." https://omanscience.com/en/articles/a-proof-of-concept-study-of-weakly-supervised-labeling-of-fine-grained-eeg-components-for-artifact-attenuation.
Harvard
Wang-Nöth, L., Huang, H., Heiler, P., Wu, S., Zhang, L. and Mayer, H. (2026) 'A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation', Available at: https://omanscience.com/en/articles/a-proof-of-concept-study-of-weakly-supervised-labeling-of-fine-grained-eeg-components-for-artifact-attenuation.
Vancouver
Wang-Nöth L, Huang H, Heiler P, Wu S, Zhang L, Mayer H. A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation. https://omanscience.com/en/articles/a-proof-of-concept-study-of-weakly-supervised-labeling-of-fine-grained-eeg-components-for-artifact-attenuation
IEEE
L. Wang-Nöth, H. Huang, P. Heiler, S. Wu, L. Zhang, and H. Mayer, "A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation," https://omanscience.com/en/articles/a-proof-of-concept-study-of-weakly-supervised-labeling-of-fine-grained-eeg-components-for-artifact-attenuation.