AttentivU: An EEG-Based Closed-Loop Biofeedback System for Real-Time Monitoring and Improvement of Engagement for Personalized Learning

Kosmyna, Nataliya; Maes, Pattie · 2019 · Crossref

DOI: 10.3390/s19235200

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Summary

This paper introduces AttentivU, a closed-loop biofeedback system designed to monitor and improve user engagement in learning environments through real-time physiological sensing. The research addresses the challenge of maintaining sustained attention in increasingly complex and distracting settings. While prior work has utilized electroencephalography (EEG) to measure engagement, existing systems rarely provide immediate feedback to the user themselves, leaving the feedback loop open. AttentivU aims to close this loop by delivering subtle haptic cues when engagement drops, thereby redirecting the user’s focus and potentially enhancing learning outcomes. The system comprises two primary components: an EEG headband (BrainCo Focus 1) for measuring cognitive engagement and a scarf containing vibromotors for delivering haptic feedback. Engagement is quantified using an "engagement index" derived from EEG power spectral density, calculated as the ratio of beta power to the sum of alpha and theta power ($E = \beta/(\alpha + \theta)$). This metric reflects the balance between active cognitive processing and rest or low-vigilance states. The signal processing pipeline involves notch filtering, band-pass filtering (4–20 Hz), and smoothing to generate a stable engagement index every 15 seconds. When a drop in engagement is detected, the scarf delivers a one-second, low-intensity vibration (0.3 g) to the upper chest area, chosen for its subtlety and privacy compared to visual or auditory feedback. The authors evaluated AttentivU in two studies involving 48 adults across different learning scenarios: watching video lectures and attending face-to-face lectures. The experimental design included three conditions: (1) biofeedback, where vibrations were triggered by detected drops in engagement; (2) random feedback, where vibrations occurred independently of engagement levels; and (3) no feedback, serving as a control. The studies aimed to determine whether real-time, engagement-contingent haptic feedback could effectively redirect attention and improve comprehension. The results demonstrated that the biofeedback condition successfully redirected participants’ engagement back to the task at hand. Furthermore, participants in the biofeedback group showed improved performance on comprehension tests compared to those in the random feedback and no-feedback conditions. These findings suggest that closed-loop biofeedback systems can effectively augment learning by providing immediate, non-intrusive cues that help users self-regulate their attention. The study highlights the potential of wearable EEG technology combined with haptic feedback to support personalized learning and improve cognitive performance in real-world educational settings.

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• neuro workload indices