用脑电波识别学生在线学习专注度，准确率超97%。
Assessing a Single Student's Concentration on Learning Platforms: A Machine Learning-Enhanced EEG-Based Framework
- 基于脑电数据提取50个特征，定制化训练随机森林模型。
- 在电脑和虚拟现实场景中分别达到97.6%和98%准确率。
- 适合教育科技、个性化学习系统研发者参考。
本研究提出一种针对个体学生在线学习专注状态的分类框架，通过定制化机器学习模型实现。详细描述了脑电数据（EEG）的采集与预处理流程，并从α、β、θ、δ、γ五个频段提取50个统计特征。经特征选择优化后，采用个性化随机森林模型进行分析。实验使用Muse头戴设备（第二代），配备五个电极（TP9, AF7, AF8, TP10, NZ），记录学生在计算机和虚拟现实平台学习时的脑电信号。结果显示，在计算机学习场景下测试准确率达97.6%，虚拟现实场景下达98%。该结果证明所提方法在个性化学习专注度监测中的有效性。
原文摘要 · Abstract (English)
This study introduces a specialized pipeline designed to classify the concentration state of an individual student during online learning sessions by training a custom-tailored machine learning model. Detailed protocols for acquiring and preprocessing EEG data are outlined, along with the extraction of fifty statistical features from five EEG signal bands: alpha, beta, theta, delta, and gamma. Following feature extraction, a thorough feature selection process was conducted to optimize the data inputs for a personalized analysis. The study also explores the benefits of hyperparameter fine-tuning to enhance the classification accuracy of the student's concentration state. EEG signals were captured from the student using a Muse headband (Gen 2), equipped with five electrodes (TP9, AF7, AF8, TP10, and a reference electrode NZ), during engagement with educational content on computer-based e-learning platforms. Employing a random forest model customized to the student's data, we achieved remarkable classification performance, with test accuracies of 97.6% in the computer-based learning setting and 98% in the virtual reality setting. These results underscore the effectiveness of our approach in delivering personalized insights into student concentration during online educational activities.
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