S. Moontaha, A. Kappattanavar, Pascal Hecker, B. Arnrich
{"title":"Wearable EEG-Based Cognitive Load Classification by Personalized and Generalized Model Using Brain Asymmetry","authors":"S. Moontaha, A. Kappattanavar, Pascal Hecker, B. Arnrich","doi":"10.5220/0011628300003414","DOIUrl":null,"url":null,"abstract":": EEG measures have become prominent with the increasing popularity of non-invasive, portable EEG sensors for neuro-physiological measures to assess cognitive load. In this paper, utilizing a four-channel wearable EEG device, the brain activity data from eleven participants were recorded while watching a relaxation video and performing three cognitive load tasks. The data was pre-processed using outlier rejection based on a movement filter, spectral filtering, common average referencing, and normalization. Four frequency-domain feature sets were extracted from 30-second windows encompassing the power of δ , θ , α , β and γ frequency bands, the respective ratios, and the asymmetry features of each band. A personalized and generalized model was built for the binary classification between the relaxation and cognitive load tasks and self-reported labels. The asymmetry feature set outperformed the band ratio feature sets with a mean classification accuracy of 81.7% for the personalized model and 78% for the generalized model. A similar result for the models from the self-reported labels necessitates utilizing asymmetry features for cognitive load classification. Extracting high-level features from asymmetry features in the future may surpass the performance. Moreover, the better performance of the personalized model leads to future work to update pre-trained generalized models on personal data.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"35 1","pages":"41-51"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0011628300003414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
: EEG measures have become prominent with the increasing popularity of non-invasive, portable EEG sensors for neuro-physiological measures to assess cognitive load. In this paper, utilizing a four-channel wearable EEG device, the brain activity data from eleven participants were recorded while watching a relaxation video and performing three cognitive load tasks. The data was pre-processed using outlier rejection based on a movement filter, spectral filtering, common average referencing, and normalization. Four frequency-domain feature sets were extracted from 30-second windows encompassing the power of δ , θ , α , β and γ frequency bands, the respective ratios, and the asymmetry features of each band. A personalized and generalized model was built for the binary classification between the relaxation and cognitive load tasks and self-reported labels. The asymmetry feature set outperformed the band ratio feature sets with a mean classification accuracy of 81.7% for the personalized model and 78% for the generalized model. A similar result for the models from the self-reported labels necessitates utilizing asymmetry features for cognitive load classification. Extracting high-level features from asymmetry features in the future may surpass the performance. Moreover, the better performance of the personalized model leads to future work to update pre-trained generalized models on personal data.