https://scholars.lib.ntu.edu.tw/handle/123456789/630396
標題: | An Effective Entropy-Assisted Mind-Wandering Detection System Using EEG Signals of MM-SART Database | 作者: | Chen, Yi Ta Lee, Hsing Hao Shih, Ching Yen Chen, Zih Ling Beh, Win Ken SU-LING YEH AN-YEU(ANDY) WU |
關鍵字: | Correlation importance feature selection (CIFE) | EEG | entropy features | mind-wandering | sustained attention to response task (SART) | 公開日期: | 1-八月-2022 | 出版社: | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | 卷: | 26 | 期: | 8 | 起(迄)頁: | 3649 | 來源出版物: | IEEE Journal of Biomedical and Health Informatics | 摘要: | Mind-wandering (MW), which is usually defined as a lapse of attention has negative effects on our daily life. Therefore, detecting when MW occurs can prevent us from those negative outcomes resulting from MW. In this work, we first collected a multi-modal Sustained Attention to Response Task (MM-SART) database for MW detection. Eighty-two participants' data were collected in our dataset. For each participant, we collected 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 the EEG signals, we utilize entropy-based features. 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 propose correlation importance feature elimination (CIFE) along with AUC-based channel selection. By using two most significant EEG channels, we can reduce the training time of the classifier by 44.16%. By applying 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). Finally, we can apply the current work to educational scenarios nowadays, especially in remote learning systems. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/630396 | ISSN: | 21682194 | DOI: | 10.1109/JBHI.2022.3187346 |
顯示於: | 電機工程學系 |
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