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

TL;DR

The paper tackles the challenge of real-time concentration monitoring in online learning by leveraging a consumer-grade EEG headband (FocusCalm) and a full preprocessing–feature–classification pipeline. It introduces intra-video ground-truth labeling, a Wavelet Packet Transform–based feature set (466 features, plus 8 SDK features), and a two-stage feature selection (PCF plus RFE-SVM) to build an SVM classifier with an RBF kernel evaluated under leave-one-subject-out cross-validation, achieving 88.77% accuracy. A real-time neurofeedback system with a PyQt6 GUI demonstrates practical applicability, and a pilot study shows statistically significant improvements in concentration during feedback (t = 5.73, p = 0.007). The work demonstrates the feasibility of real-time, user-friendly BCI for online learning using consumer devices, with potential extensions to multi-channel and multi-modal inputs.

Abstract

The prevalence of online learning poses a vital challenge in real-time monitoring of students' concentration. Traditional methods such as questionnaire assessments require manual intervention, and webcam-based monitoring fails to provide accurate insights about learners' mental focus as it is 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 were 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 was found to be 88.77%. The system provides feedback alerts upon detection of a non-attention state and maintains focus profile logs. A pilot study was conducted to evaluate the effectiveness of real-time feedback. Five participants underwent a 10-minute session comprising a 5-minute baseline phase devoid of feedback, succeeded by a 5-minute feedback phase, during which alerts were activated if participants exhibited inattention 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.

Figures (5)

• Figure 1: Positions of Fpz, Fp1 and Fp2 electrodes used by FocusCalm headband according to the international 10-10 electrode placement system.
• Figure 2: Overview of the data collection paradigm and validation procedure.
• Figure 3: Subfigures (a) and (b) illustrate the effect of trimming on filtered EEG signals, showing signals without trimming in (a) and with trimming in (b). Subfigure (c) presents the data segmentation pipeline using overlapping windows, highlighting how trimming affects the resulting segments.
• Figure 4: Accuracy of the model evaluated using Leave-One-Subject-Out (LOSO) cross-validation, where each iteration treats one subject as the test fold and the remaining subjects as the training set.
• Figure 5: Subfigures (a) and (b) represent the graphical user interface and the real-time working pipeline. Subfigure (c) denotes the changes between attentive and non-attentive states during the real-time system test on five participants.