[
    {
        "id": "osp-16666",
        "type": "article-journal",
        "title": "A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation",
        "author": [
            {
                "family": "Wang-Nöth",
                "given": "Lu"
            },
            {
                "family": "Huang",
                "given": "Hai"
            },
            {
                "family": "Heiler",
                "given": "Philipp"
            },
            {
                "family": "Wu",
                "given": "Shuqiong"
            },
            {
                "family": "Zhang",
                "given": "Liyun"
            },
            {
                "family": "Mayer",
                "given": "Helmut"
            }
        ],
        "URL": "https://omanscience.com/en/articles/a-proof-of-concept-study-of-weakly-supervised-labeling-of-fine-grained-eeg-components-for-artifact-attenuation",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]