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|Title:||Generalizability of EEG-based mental attention modeling with multiple cognitive tasks||Authors:||Phyo Wai, Aung Aung
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2020||Source:||Phyo Wai, A. A., Dou, M. & Guan, C. (2020). Generalizability of EEG-based mental attention modeling with multiple cognitive tasks. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2959-2962. https://dx.doi.org/10.1109/EMBC44109.2020.9176346||Abstract:||Attention is the foundation of a person's cognitive function. The attention level can be measured and quantified from the electroencephalogram (EEG). For the study of attention detection and quantification, we researchers usually ask the subjects to perform a cognitive test with distinct attentional and inattentional mental states. Different attention tasks are available in the literature, but there is no empirical evaluation to quantitatively compare the attention detection performance among the tasks. We designed an experiment with three typical cognitive tests: Stroop, Eriksen Flanker, and Psychomotor Vigilance Task (PVT), which are arranged in a random order in multiple trials. Data were collected from ten subjects. We used six standard band power features to classify the attention levels in four evaluation scenarios for both subject-specific and subject-independent cases. With cross-validation for the subject-independent case, we achieved a classification accuracy of 61.6%, 63.7% and 65.9% for PVT, Stroop and Flanker tasks respectively. We achieved the highest accuracy of 74.1% and 65.9% for the Flanker test in the subject-dependent and subject-independent cases respectively. Our evaluation shows no statistically significant differences in classification accuracy among the three distinct cognitive tasks. Our study highlights that EEG-based attention recognition can generalize across subjects and cognitive tasks.||URI:||https://hdl.handle.net/10356/147512||ISSN:||2694-0604||DOI:||10.1109/EMBC44109.2020.9176346||DOI (Related Dataset):||10.1109/EMBC44109.2020.9176346||Rights:||© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/EMBC44109.2020.9176346||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Conference Papers|
Updated on Jun 20, 2022
Updated on Jun 20, 2022
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