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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.
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