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

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.