NeuroPilot: A Realtime Brain-Computer Interface system to enhance concentration of students in online learning (2510.20958v1)

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.