Reproduce “A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation” on OpenResearch
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About this paper
Lu Wang-Nöth, Hai Huang, Philipp Heiler, Shuqiong Wu, Liyun Zhang, Helmut Mayer
The authors present a method for reducing facial muscle artifacts in EEG by splitting source components into frequency-specific pieces and training a weakly supervised multiple-instance learning model on epoch labels to score them. In held-out data, attenuation was associated with reduced spectral deviation from an eyes-open reference, most clearly for jaw tension; the reference is not ground truth.
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