用脑电仪实时监测在线学习专注度，反馈能显著提升学生注意力。
NeuroPilot: A Realtime Brain-Computer Interface system to enhance concentration of students in online learning
- 通过脑电头带采集注意力与分心状态的脑波数据。
- 系统识别准确率达88.77%，并可实时发出注意力下降警报。
- 适合教育科技、脑机接口应用研究者参考。
在线学习普及带来实时监测学生专注度的挑战。传统问卷需人工干预，摄像头监控易受视线固定干扰，无法反映真实认知投入；现有脑机接口方法缺乏实时验证。为此，研究采用非侵入式脑电头带FocusCalm，采集20名参与者观看教育视频时20分钟的脑波数据。通过视频内嵌问卷进行数据有效性验证，信号经滑动窗口分割、巴特沃斯带通滤波及眼动伪影去除后，提取时域、频域、小波与统计特征，结合递归特征消除与支持向量机分类，实现注意力状态识别。留一被试交叉验证准确率达88.77%。系统可实时触发注意力下降警报，并记录专注度日志。小规模试点中，5名参与者经历5分钟无反馈基线期和5分钟有警报反馈期（连续8秒不专注即触发），配对t检验显示反馈期专注度显著提升（t = 5.73, p = 0.007）。
原文摘要 · Abstract (English)
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.
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