EEG-based assessment of long-term vigilance and lapses of attention using a user-centered frequency-tagging approach

Ladouce, S; Torre Tresols, J J; Goff, K Le; Dehais, F · 2025 · Crossref

DOI: 10.1088/1741-2552/add771

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Summary

This study addresses the challenge of monitoring vigilance and detecting attentional lapses during prolonged, monotonous tasks, where traditional EEG markers like alpha and theta power often lack specificity and temporal stability. The authors propose a user-centered approach using Steady-State Visual Evoked Potentials (SSVEPs) elicited by a minimally intrusive frequency-tagging flicker. The primary objective was to determine if continuous SSVEP responses could reliably distinguish between periods of successful attention and attentional lapses without compromising task performance or user experience. The experiment involved 16 healthy participants who completed two 45-minute sessions of the Mackworth Clock Task, a sustained visual attention paradigm. In one session, a 14 Hz flicker was superimposed on the task display; in the other, no flicker was present. The flicker was designed with low luminance and contrast to remain imperceptible and non-distracting. EEG data were recorded using 32 electrodes and processed using Rhythmic Entrainment Source Separation (RESS) to extract the SSVEP signal-to-noise ratio (SNR). This metric was compared against individual alpha peak frequency (IAPF) and theta band activity. Behavioral data included reaction times and missed target events, while subjective questionnaires assessed sleepiness, eye strain, fatigue, and workload. Results indicated that the SSVEP SNR was significantly lower in the periods preceding attentional lapses (missed events) compared to successful target detections. In contrast, individual alpha peak and theta band power did not reliably differentiate between hit and miss trials. Crucially, the presence of the flicker did not alter task performance, such as reaction times or accuracy, nor did it negatively impact subjective user experience measures like fatigue or eye strain. The SSVEP-based measure outperformed traditional spectral markers in identifying attentional lapses. The findings suggest that a low-contrast, continuous frequency-tagging flicker provides a robust, non-intrusive physiological marker for real-time vigilance monitoring. This approach offers a practical solution for integrating passive brain-computer interfaces into high-stakes environments, such as air traffic control or driving, where detecting attentional drift is critical for safety. By demonstrating that SSVEP dynamics can predict lapses without interfering with task execution, the study advances the development of neuroadaptive technologies that can continuously assess cognitive engagement during extended monitoring duties.

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Topics

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• neuro workload indices
• vigilance
• sustained attention vigilance
• drowsiness detection algorithms
• microsleep

Information type

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• Empirical Findings: physiological data