{
  "abstract": "Eye blinks contaminate frontal electroencephalography (EEG), while asymmetric disturbances can cause false detections. We present a physiology-informed detector that combines waveform correlation, peak-time agreement, amplitude similarity, and symmetric-component dominance in an explicit event-verification rule. It uses two frontal channels without supervised model fitting. Evaluation on raw ZuCo 1.0 recordings included 260 eligible blocks from 12 participants, totalling 1291.61 min and 8611 eye-tracker reference intervals. Scalp and periocular pairs were evaluated separately. On the scalp pair, participant-mean precision was 0.731, recall 0.774, F1 0.734, and false positives 1.469 per minute. At fixed settings, false-positive rates were lower than with single-channel thresholding and bilateral averaging (both Holm-adjusted p = 0.005859), and remained lower than those of four rule-based controls across five perturbation conditions. Threshold-selected simple controls achieved higher precision at similar recall. Component ablations quantified the effects of the verification checks on sensitivity and false positives. The offline Python detector required a median of 2.13 ms per minute of recorded data on an Apple M1 Pro. These results establish a computationally inexpensive approach that makes blink-event decisions directly inspectable through bilateral signal measurements.",
  "abstract_shown": true,
  "affiliations": [
    "College of Biomedical Engineering, , Shanghai 200433, China;",
    "Human Phenome Institute, , Shanghai 201203, China (Y.Z.);",
    "Department of Cardiovascular Surgery, Shanghai General Hospital, School of Medicine, Shanghai 200080, China; (J.C.); (F.S.)",
    "Micro-Nano System Centre, School of Information Science and Technology, , Shanghai 200433, China;"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Weiqiang Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Menglong Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Yujie Zhang"
    },
    {
      "affs": [
        2
      ],
      "name": "Jingyi Cao"
    },
    {
      "affs": [
        2
      ],
      "name": "Feng Shen"
    },
    {
      "affs": [
        3
      ],
      "name": "Xiaohui Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Chen Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Yang Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/btvjyr1ggp4m4.bib",
  "cited_by": 0,
  "doi": "10.3390/bioengineering13101208",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13701908/?tool=EBI",
  "id": "btvjyr1ggp4m4",
  "issns": [
    "2306-5354"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13701908",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QH301-705.5"
    ],
    [
      "lcc",
      "T"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7222589038",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13701908/pdf/?tool=EBI",
  "pmcid": "PMC13701908",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/btvjyr1ggp4m4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "PMC:PMC13701908",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13701908"
    },
    {
      "first_seen": "2026-10-31T14:21:22Z",
      "native_id": "021527805e284eb7a4ddf1693bc239f4",
      "source": "doaj",
      "url": "https://doi.org/10.3390/bioengineering13101208"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|EEG and Brain-Computer Interfaces",
      "subfield": "neuroscience"
    }
  ],
  "title": "Physiology-Informed Blink Artefact Detection from Bilateral Frontal EEG Under Asymmetric Interference",
  "title_text": "Physiology-Informed Blink Artefact Detection from Bilateral Frontal EEG Under Asymmetric Interference",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:21:22Z",
  "url": "https://research.draconitic.com/paper/btvjyr1ggp4m4",
  "venue": "Bioengineering (Basel, Switzerland)",
  "versions": []
}