基于MM-SART数据库的EEG信号走神检测系统
An Effective Entropy-assisted Mind-wandering Detection System with EEG Signals based on MM-SART Database
摘要：
走神通常被定义为注意力的丧失，发生在20%-40%的时间内，对我们的日常生活产生负面影响。因此，检测MW何时发生可以防止MW导致的负面结果，例如在学习过程中无法跟踪课程。在这项工作中，我们首先收集了一个多模态持续注意反应任务（MM-SART）数据库检测MW。在我们的实验中收集了82名参与者的数据。对于每个参与者，我们收集了32通道脑电图（EEG）信号，光电体积描记（PPG）信号，皮肤电反应（GSR）信号，眼动仪信号和几份问卷的测量结果进行详细分析。然后，我们提出了一个有效的MW检测系统的基础上收集的脑电信号。为了探索脑电信号的非线性特征，我们利用基于熵的特征在时间，频率和小波域。实验结果表明，我们可以达到0.712 AUC得分使用随机森林（RF）分类器与留一主题交叉验证。此外，为了降低MW检测系统的整体计算复杂度，我们应用通道选择和特征选择技术。通过只使用两个最重要的EEG通道，我们可以减少44.16%的分类器的训练时间。通过对特征集执行相关重要性特征消除（CIFE），我们可以进一步将AUC得分提高到0.725，但与递归特征消除（RFE）方法相比，选择时间仅为14.6%。通过提出MW检测引擎，当前的工作可以应用于教育场景，特别是在当今的远程学习时代。
英文摘要：
Mind-wandering (MW), which usually defined as a lapse of attention, occurs between 20%-40% of the time, has negative effects on our daily life. Therefore, detecting when MW occurs can prevent us from those negative outcomes resulting from MW, such as failing to keep track of course during learning. In this work, we first collect a multi-modal Sustained Attention to Response Task (MM-SART) database for detecting MW. Eighty-two participants' data are collected in our experiments. For each participant, we collect measures of 32-channels electroencephalogram (EEG) signals, photoplethysmography (PPG) signals, galvanic skin response (GSR) signals, eye tracker signals, and several questionnaires for detailed analyses. Then, we propose an effective MW detection system based on the collected EEG signals. To explore the non-linear characteristics of EEG signals, we utilize the entropy-based features in time, frequency, and wavelet domains. The experimental results show that we can reach 0.712 AUC score by using the random forest (RF) classifier with the leave-one-subject-out cross-validation. Moreover, to lower the overall computational complexity of the MW detection system, we apply techniques of channel selection and feature selection. By using the only two most significant EEG channels, we can reduce the training time of the classifier by 44.16%. By performing correlation importance feature elimination (CIFE) on the feature set, we can further improve the AUC score to 0.725 but with only 14.6% of the selection time compared with the recursive feature elimination (RFE) method. By proposing the MW detection engine, current work can be applied to educational scenarios, especially in the era of remote learning nowadays.
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