EEG microstate analysis of emotion regulation reveals no sequential processing of valence and emotional arousal

Abstract

In electroencephalography (EEG), microstates are distributions of activity across the scalp that persist for several tens of milliseconds before changing into a different pattern. Microstate analysis is a way of utilizing EEG as both temporal and spatial imaging tool, but has rarely been applied to task-based data. This study aimed to conceptually replicate microstate findings of valence and emotional arousal processing and investigate the effects of emotion regulation on microstates, using data of an EEG paradigm with 107 healthy adults who actively viewed emotional pictures, cognitively detached from them, or suppressed facial reactions. Within the first 600 ms after stimulus onset only the comparison of viewing positive and negative pictures yielded significant results, caused by different electrodes depending on the microstate. Since the microstates associated with more and less emotionally arousing pictures did not differ, sequential processing could not be replicated. When extending the analysis to 2000 ms after stimulus onset, differences were exclusive to the comparison of viewing and detaching from negative pictures. Intriguingly, we observed the novel phenomenon of a microstate difference that could not be attributed to single electrodes. This suggests that microstate analysis can detect differences beyond those detected by event-related potential analysis.

Changes in microstates of first-episode untreated nonsuicidal self-injury adolescents exposed to negative emotional stimuli and after receiving rTMS intervention

“…However, there is no consensus on the classification of microstates. Notably, EEG microstates have been studied in depth both during task performance and in the resting state (i.e., in the absence of a task) ( 38 ). Unlike resting-state EEG microstates, task-state EEG microstates have more than four classifications, which are associated with the task, and the polarity of the topographic map and the time course of the EEG ( 23 , 24 ).…”

A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis

“…EEG microstate analysis is a strong tool to study the temporal and spatial dynamics of human brain activity 61 . Microstate analysis reflects cortical activation for quasi-stable states in 60–120 ms which is important for investigating brain dynamics 62 .…”

“…For example, Lehmann et al 28 – 30 demonstrate that the existence of a quasi-stable microstate by segmenting spontaneous EEG at the sub-second level produces stable and evenly patterned results at 80–120 ms intervals. Many previous researchers have revealed microstates change in various diseases and mental states such as anxiety disorder 31 , neurodegenerative disorder 32 – 34 , sleep 35 , mood disorder 36 , schizophrenia 37 , and emotion revelation 38 , physical exercise 39 , insomnia 40 , hearing loss 41 . Since the EEG microstates provide different states of brain activity with high interpretability (i.e., A, B, C, and D are known to be associated with auditory, visual, default mode, and dorsal attention), it seems to be advantageous to use this feature in exploring the selective auditory attention detection.…”

EEG Microstates in Social and Affective Neuroscience

“…Microstate analysis of ERPs enables the researcher to identify, time, and sequence neurophysiological processes across distinct experimental conditions or trial types, such as anxiety vs. no-anxiety (Nash et al 2023 ), conditioned fear vs. safety stimuli (Pizzagalli et al 2003 ; Mueller and Pizzagalli 2016 ), direct vs. averted gaze (Burra et al 2016 ), honest vs. dishonest decisions (Globig et al 2023 ), ingroup- vs. outgroup-related information (Walker et al 2008 ; Schiller et al 2020a ), less vs. more attractive faces (Han et al 2020 , 2022 ), self- vs. other-voice processing (Iannotti et al 2022 ), social vs. non-social stimuli/contexts (Thierry et al 2006 ; Ortigue et al 2009 , 2010 ; Cacioppo et al 2012 , 2015 , 2016 , 2018 ; Koban et al 2012 ; Decety and Cacioppo 2012 ; Pegna et al 2015 ), stereotype-congruent vs. stereotype-incongruent information (Schiller et al 2016 ), stress vs. no stress (Schiller et al 2023a ) and neutral vs. emotional stimuli (Pizzagalli et al 2000 ; Gianotti et al 2007 , 2008 ; Cacioppo et al 2016 ; Tanaka et al 2021 ; Zerna et al 2021 ; Liang et al 2022 ; Prete et al 2022 ) (Fig. 3 ).…”