Analysis of EEG microstates as biomarkers in neuropsychological processes – Review

Microstate analysis is a spatiotemporal method where instantaneous scalp potential topography represents the current state of the brain. The temporal evolution of these scalp topographies gives an understanding of quasi-stable periods of long-range coherence between distant electrodes, reflecting functional coordination within large-scale cortical networks. It has been proven potential in identification and characterization of neurophysiological indicators associated with neuropsychiatric conditions. Changes in microstates connected to symptoms and cognitive impairments of neuropsychiatric conditions. It is useful in the study of cognitive processes and disorders related to memory. Researchers may probe into the relationships between microstates and other cognitive processes, such as memory retrieval and encoding. This is a tool for clinicians to enhance the precision of diagnosis and inform possibilities for treatment by acquiring information regarding individual diversity in microstates could lead to tailored medical methods. Customizing treatment according to a patient's microstate patterns could improve the efficacy of treatment. The papers selected for the review span a broad-spectrum including memory related disorders, psychiatry and neurological disorders. A section in the review article has been dedicated to source localization of EEG microstates. The selection of review papers shed light on the importance and huge potential of application of EEG microstate analysis in various neuropsychological processes. The review concludes with the need for standardization of microstate analysis. It suggests the incorporation of widely accepted machine learning techniques for increasing the accuracy, reliability and acceptability of microstate analysis as reliable biomarkers for neurological conditions in the future. •The temporal evolution of scalp topographies gives an understanding of quasi-stable periods of long-range coherence between distant electrodes, reflecting functional coordination within large-scale cortical networks.•The papers selected for the review span a broad-spectrum including memory related disorders, psychiatry, neurological disorders including epilepsy, sleep and drowsiness and human consciousness.•A section in the review article has been dedicated to source localization of EEG microstates.•The review concludes with the suggestion that the incorporation of widely accepted machine learning techniques for increasing the accuracy, reliabilit