NeuroPilot: A Realtime Brain-Computer Interface system to enhance concentration of students in online learning

2025-10-23

AI Summary

To address the lack of real-time, objective assessment of cognitive focus in online learning, this paper proposes a non-invasive, real-time brain–computer interface (BCI) system based on an EEG headband (FocusCalm). We introduce an intra-video questionnaire labeling method to enable fine-grained ground-truth annotation of EEG data. Preprocessing involves sliding-window segmentation, Butterworth bandpass filtering, EOG artifact removal, and extraction of multimodal features—including time-domain, frequency-domain, wavelet, and statistical descriptors. Classification is performed using support vector machines (SVM) with recursive feature elimination (RFE). Under leave-one-subject-out (LOSO) cross-validation, the system achieves 88.77% accuracy. A pilot study with five participants demonstrates that real-time BCI feedback significantly improves sustained attention (p = 0.007). This work is the first to integrate intra-video validation with lightweight, closed-loop BCI feedback in online learning—ensuring both methodological rigor and practical applicability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Online Learning & Bandits

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Prevalence of online learning poses a vital challenge in real-time monitoring of students' concentration. Traditional methods such as questionnaire assessments require manual interventions and webcam-based monitoring fails to provide accurate insights into learners' mental focus as they are deceived by mere screen fixation without cognitive engagement. Existing BCI-based approaches lack real-time validation and evaluation procedures. To address these limitations, a Brain-Computer Interface (BCI) system is developed using a non-invasive Electroencephalogram (EEG) headband, FocusCalm, to record brainwave activity under attentive and non-attentive states. 20 minutes of data were collected from each of 20 participants watching a pre-recorded educational video. The data validation employed a novel intra-video questionnaire assessment. Subsequently, collected signals were segmented (sliding window), filtered (butterworth bandpass), and cleaned (removal of high-amplitude and EOG artifacts such as eye blinks). Time, frequency, wavelet and statistical features have been extracted, followed by recursive feature elimination (RFE) with Support vector machines (SVMs) to classify attention and non-attention states. The leave-one-subject-out (LOSO) cross-validation accuracy has been tested to be 88.77%. The system provides feedback alerts upon non-attention state detection and keeps focus profile logs. A pilot study was conducted to evaluate the effectiveness of real-time feedback. Five participants completed a 10-minute session consisting of a 5-minute baseline phase without feedback followed by a 5-minute feedback phase, during which alerts were issued if participants remained non-attentive for approximately 8 consecutive seconds. A paired t-test (t = 5.73, p = 0.007) indicated a statistically significant improvement in concentration during the feedback phase.

Problem

• Monitoring student concentration in online learning environments
• Validating real-time brain-computer interface systems for attention detection
• Providing immediate feedback to enhance learner focus during online education

Innovation

• Non-invasive EEG headband monitors brainwave activity
• Recursive feature elimination with SVM classifies attention states
• Real-time feedback alerts enhance student concentration