Resting state EEG microstate profiling and a machine-learning based classifier model in epilepsy.

ABSTRACT: Electroencephalography-based (EEG) microstate analysis is a promising and widely studied method in which spontaneous cerebral activity is segmented into sub second level quasi-stable states and analyzed. Currently it is being widely explored due to increasing evidence of the association of microstates with cognitive functioning and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). In our study using the four archetypal microstates (A, B, C and D), we investigated the changes in resting state EEG microstate dynamics in persons with temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) compared to healthy controls (HC). Machine learning was applied to study its feasibility in differentiating between different groups using microstate stati

SUBMITTER: Sa A 

PROVIDER: S-EPMC11564422 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature