Enhancing Sustained Attention

Demazure, Théophile; Karran, Alexander; Léger, Pierre-Majorique; Labonté-LeMoyne, Élise; Sénécal, Sylvain; Fredette, Marc; Babin, Gilbert · 2021 · Crossref

DOI: 10.1007/s12599-021-00701-3

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Summary

This pilot study addresses the challenge of sustaining human attention during extended monitoring tasks within automated enterprise systems. As automation transforms operational roles into monitoring duties, operators face significant risks of attentional decrements, leading to increased errors and reduced performance. The research investigates whether integrating a passive Brain-Computer Interface (BCI) into an Enterprise Information System (EIS) can help users regulate their sustained attention (SA) in real-time, thereby improving task performance and reducing on-task errors. The study is grounded in the motivational control theory of cognitive fatigue and closed-loop control theory, proposing that users can voluntarily recruit cognitive resources to maintain attention when provided with appropriate neurofeedback. The researchers developed a prototype artifact using a design science research methodology. The system integrates a passive BCI with a simulated business logistics task based on the ERPsim platform, which mimics a real-world enterprise resource planning environment. Participants performed a 90-minute monitoring task involving periodic decision-making cycles regarding stock and sales across three regions. The BCI utilized electroencephalography (EEG) to measure brain activity, specifically calculating an "engagement index" derived from beta, alpha, and theta frequency bands ($\beta/(\alpha + \theta)$). To address signal volatility, the authors introduced a novel classification algorithm called Threshold Reactive Adaptive Dynamic Spectrum (ThReADS). This algorithm processed EEG data in real-time to determine the user's attention state and provided unobtrusive visual neurofeedback via a color gradient background on the task interface, signaling whether the user was in a "decision-ready," "unfocused," or "critical" attention state. The experimental results demonstrated that the BCI-assisted condition allowed users to positively regulate their sustained attention compared to baseline expectations. Participants using the BCI exhibited higher levels of sustained attention throughout the task. This enhanced cognitive state was associated with increased on-task action and a small but notable reduction in on-task errors. The study confirms the feasibility of using a passive BCI to detect and mitigate attentional lapses in ecologically valid enterprise settings. The findings suggest that neuroadaptive systems can effectively support human operators in high-stakes, long-duration monitoring tasks by providing real-time feedback that encourages self-regulation of cognitive effort. The significance of this work lies in its contribution to the fields of NeuroIS and neuroergonomics, offering prescriptive knowledge for designing BCI artifacts for enterprise applications. By validating the use of the engagement index with dynamic thresholds, the study provides a robust method for assessing sustained attention in real-world contexts. The results imply that integrating neurophysiological feedback into information systems can enhance human-machine collaboration, particularly in environments where automation shifts the human role toward vigilant monitoring. Future research directions include refining the classification algorithms and exploring broader applications of such neuroadaptive systems in complex enterprise environments.

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• neuro workload indices
• sustained attention vigilance

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• Theoretical Contribution: theory or model