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Peer-reviewedCC BYBioengineering (Basel, Switzerland)

Physiology-Informed Blink Artefact Detection from Bilateral Frontal EEG Under Asymmetric Interference

  • Weiqiang Zhang1
  • Menglong Li2
  • Yujie Zhang2
  • Jingyi Cao3
  • Feng Shen3
  • Xiaohui Liu4
  • Chen Chen2
  • Yang Li2
4 affiliations
  1. 1College of Biomedical Engineering, , Shanghai 200433, China;
  2. 2Human Phenome Institute, , Shanghai 201203, China (Y.Z.);
  3. 3Department of Cardiovascular Surgery, Shanghai General Hospital, School of Medicine, Shanghai 200080, China; (J.C.); (F.S.)
  4. 4Micro-Nano System Centre, School of Information Science and Technology, , Shanghai 200433, China;
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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.

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Cite

BibTeXRIS
BibTeX
@article{zhang2026physiologyinformed,
  title = {{Physiology-Informed Blink Artefact Detection from Bilateral Frontal EEG Under Asymmetric Interference}},
  author = {Weiqiang Zhang and Menglong Li and Yujie Zhang and Jingyi Cao and Feng Shen and Xiaohui Liu and Chen Chen and Yang Li},
  journal = {Bioengineering (Basel, Switzerland)},
  year = {2026},
  month = oct,
  doi = {10.3390/bioengineering13101208},
  url = {https://europepmc.org/article/PMC/PMC13701908},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Physiology-Informed Blink Artefact Detection from Bilateral Frontal EEG Under Asymmetric Interference
AU  - Weiqiang Zhang
AU  - Menglong Li
AU  - Yujie Zhang
AU  - Jingyi Cao
AU  - Feng Shen
AU  - Xiaohui Liu
AU  - Chen Chen
AU  - Yang Li
PY  - 2026
DA  - 2026/10/01
JO  - Bioengineering (Basel, Switzerland)
DO  - 10.3390/bioengineering13101208
UR  - https://europepmc.org/article/PMC/PMC13701908
AB  - 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.
SN  - 2306-5354
ER  -