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Mind-wandering (MW), which usually defined as a lapse of attention, occursbetween 20%-40% of the time, has negative effects on our daily life. Therefore,detecting when MW occurs can prevent us from those negative outcomes resultingfrom 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 ourexperiments. For each participant, we collect measures of 32-channelselectroencephalogram (EEG) signals, photoplethysmography (PPG) signals,galvanic skin response (GSR) signals, eye tracker signals, and severalquestionnaires for detailed analyses. Then, we propose an effective MWdetection system based on the collected EEG signals. To explore the non-linearcharacteristics of EEG signals, we utilize the entropy-based features in time,frequency, and wavelet domains. The experimental results show that we can reach0.712 AUC score by using the random forest (RF) classifier with theleave-one-subject-out cross-validation. Moreover, to lower the overallcomputational complexity of the MW detection system, we apply techniques ofchannel selection and feature selection. By using the only two most significantEEG channels, we can reduce the training time of the classifier by 44.16%. Byperforming correlation importance feature elimination (CIFE) on the featureset, we can further improve the AUC score to 0.725 but with only 14.6% of theselection time compared with the recursive feature elimination (RFE) method. Byproposing the MW detection engine, current work can be applied to educationalscenarios, especially in the era of remote learning nowadays.

An Effective Entropy-assisted Mind-wandering Detection System with EEG Signals based on MM-SART Database