Complexity measures reveal age-dependent changes in electroencephalogram during working memory task
Issued Date
2024-07-26
Resource Type
ISSN
01664328
eISSN
18727549
Scopus ID
2-s2.0-85194148214
Journal Title
Behavioural Brain Research
Volume
470
Rights Holder(s)
SCOPUS
Bibliographic Citation
Behavioural Brain Research Vol.470 (2024)
Suggested Citation
Javaid H., Nouman M., Cheaha D., Kumarnsit E., Chatpun S. Complexity measures reveal age-dependent changes in electroencephalogram during working memory task. Behavioural Brain Research Vol.470 (2024). doi:10.1016/j.bbr.2024.115070 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/98576
Title
Complexity measures reveal age-dependent changes in electroencephalogram during working memory task
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Corresponding Author(s)
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Abstract
The alterations in electroencephalogram (EEG) signals are the complex outputs of functional factors, such as normal physiological aging, pathological process, which results in further cognitive decline. It is not clear that when brain aging initiates, but elderly people are vulnerable to be incipient of neurodegenerative diseases such as Alzheimer's disease. The EEG signals were recorded from 20 healthy middle age and 20 healthy elderly subjects while performing a working memory task. Higuchi's fractal dimension (HFD), Katz's fractal dimension (KFD), sample entropy and three Hjorth parameters were extracted to analyse the complexity of EEG signals. Four machine learning classifiers, multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbour (KNN), and logistic model tree (LMT) were employed to distinguish the EEG signals of middle age and elderly age groups. HFD, KFD and Hjorth complexity were found significantly correlated with age. MLP achieved the highest overall accuracy of 93.75%. For posterior region, the maximum accuracy of 92.50% was achieved using MLP. Since fractal dimension associated with the complexity of EEG signals, HFD, KFD and Hjorth complexity demonstrated the decreased complexity from middle age to elderly groups. The complexity features appear to be more appropriate indicators of monitoring EEG signal complexity in healthy aging.