Analyzing the Discriminative Power of EEG Microstates Over Mental Tasks
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Date
2023
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Analyzing the Discriminative Power of EEG Microstates Over Mental Tasks
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Authors
Vetró, Mihály
Hullám, Gábor
Hullám, Gábor
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en
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könyvfejezet
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Open access
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Szerző
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2023.02.06-2023.02.07.
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Budapest
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30th Minisymposium of the Department of Measurement and Information Systems
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978-963-421-904-0
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Proceedings of the 30th Minisymposium
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Department of Measurement and Information Systems
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Post print
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Faculty of Electrical Engineering and Informatics
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21
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EEG microstates
electroencephalography
statistics
machine learning
electroencephalography
statistics
machine learning
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Konferenciacikk
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Budapest University of Technology and Economics
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Abstract
Microstate analysis of EEG recordings has long been an instrumental tool for studying the temporal dynamics of whole-brain neuronal networks. The characteristics of EEG microstate sequences have been used mostly for diagnostic purposes, including the detection of schizophrenia, epilepsy, Alzheimer’s disease, and early dementia. Aside from diagnostics, the use of this methodology has been limited. In this study, we examine the discriminative power of EEG microstates to differentiate between mental tasks, and we assess the generalizing power of microstate representations over different subjects and recording sessions. For this purpose, we inspect both the characteristics of discrete microstate sequences, as well as various features generated from the association of detected microstates with the continuous EEG data. For demonstration purposes, we use two distinct datasets, which contain recordings from multiple subjects, while performing different mental tasks.