EEG microstate features according to performance on a mental arithmetic task

Kim, Kyungwon; Duc, Nguyen Thanh; Choi, Min Sung; Lee, Boreom

doi:10.1038/s41598-020-79423-7

Cited by 36 publications

(21 citation statements)

References 75 publications

(133 reference statements)

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“…For serial subtraction tasks, the coverage, duration, and occurrence of MS3 significantly decreased and these features of MS4 significantly increased after task manipulation. Similar results were also observed by Kim et al (Kim et al, 2021) that a significant decrease of MS3 and a significant increase of MS4 were found in good performance groups while performing mental arithmetic tasks. These findings generally suggest that the influences of cognitive task manipulation (refers to emotion induction in this work) on microstate activities are in line with task effects on the default mode network (task-negative, associated with MS3) and dorsal attention network (task-positive, associated with MS4).…”

Section: The Influence Of Emotion States On Eeg Microstate Activitiessupporting

confidence: 88%

EEG Microstate Correlates of Emotion Dynamics and Stimulation Content during Video Watching

Zhang

Zhao

et al. 2021

Preprint

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Emotions dynamically change in response to ever-changing environments. It is of great importance, both clinically and scientifically, to investigate the neural representation and evoking mechanism of emotion dynamics. But, there are many unknown places in this stream of research, such as consistent and conclusive findings are still lacking. In this work, we perform an in-depth investigation of emotion dynamics under a video-watching task by gauging the dynamic associations among evoked emotions, electroencephalography (EEG) responses, and multimedia stimulation. Here, we introduce EEG microstate analysis to study emotional EEG signals, which provides a spatial-temporal neural representation of emotion dynamics. To investigate the temporal characteristics of evoking emotions during video watching with its neural mechanism, we conduct two studies from the perspective of EEG microstates. In Study 1, the dynamic microstate activities under different emotion states and emotion levels are explored to identify EEG spatial-temporal correlates of emotion dynamics. In Study 2, the stimulation effects of multimedia content (visual and audio) on EEG microstate activities are examined to learn about the involved affective information and investigate the emotion-evoking mechanism. The results show that emotion dynamics could be well reflected by four EEG microstates (MS1, MS2, MS3, and MS4). Specifically, emotion tasks lead to an increase in MS2 and MS4 coverage but a decrease in MS3 coverage, duration, and occurrence. Meanwhile, there exists a negative association between valence and MS4 occurrence as well as a positive association between arousal and MS3 coverage and occurrence. Further, we find that MS4 and MS3 activities are significantly affected by visual and audio content, respectively. In this work, we verify the possibility to reveal emotion dynamics through EEG microstate analysis from sensory and stimulation dimensions, where EEG microstate features are found to be highly correlated to different emotion states (emotion task effect and level effect) and different affective information involved in the multimedia content (visual and audio). Our work deepens the understanding of the neural representation and evoking mechanism of emotion dynamics, which can be beneficial for future development in the applications of emotion decoding and regulation.

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Section: The Influence Of Emotion States On Eeg Microstate Activitiessupporting

confidence: 88%

EEG Microstate Correlates of Emotion Dynamics and Stimulation Content during Video Watching

Zhang

Zhao

et al. 2021

Preprint

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“…The findings seem to converge on abnormalities in a specific microstate (type C). This microstate is more prominent during rest than task performance (Kim et al., 2021; Seitzman et al., 2017), and its greater presence in the prestimulus period is associated with subjective reports of mind‐wandering and larger reaction time variations (Zanesco, Denkova, et al., 2020). The suggested neural architecture for the microstate includes key nodes for the default mode network, such as posterior cingulate and precuneus (Custo et al., 2017; Panda et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

EEG microstates suggest atypical resting‐state network activity in high‐functioning children and adolescents with autism spectrum development

Takarae

Zanesco

Keehn

et al. 2022

Developmental Science

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EEG microstates represent transient electrocortical events that reflect synchronized activities of large-scale networks, which allows investigations of brain dynamics with sub-second resolution. We recorded resting EEG from 38 children and adolescents with Autism Spectrum Development (ASD) and 48 age, IQ, sex, and handednessmatched typically developing (TD) participants. The EEG was segmented into a time series of microstates using modified k-means clustering of scalp voltage topographies.The frequency and global explained variance (GEV) of a specific microstate (type C) were significantly lower in the ASD group compared to the TD group while the duration of the same microstate was correlated with the presence of ASD-related behaviors. The duration of this microstate was also positively correlated with participant age in the TD group, but not in the ASD group. Further, the frequency and duration of the microstate were significantly correlated with the overall alpha power only in the TD group. The signal strength and GEV for another microstate (type G) was greater in the ASD group than the TD group, and the associated topographical pattern differed between groups with greater variations in the ASD group. While more work is needed to clarify the underlying neural sources, the existing literature supports associations between the two microstates and the default mode and salience networks. The current study suggests specific alterations of temporal dynamics of the resting cortical network activities as well as their developmental trajectories and relationships to alpha power, which has been proposed to reflect reduced neural inhibition in ASD.

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“…The experiment was conducted on nine healthy and young participants with no sleep problems. Kim et al [ 164 ] proposed a new EEG-based method for performance evaluation. Thirty-six subjects were tested as either excellent or bad performers.…”

Section: Bci Recent Advancementsmentioning

confidence: 99%

Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities

Yadav

Maini

2023

Multimed Tools Appl

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Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software and hardware to operate any device mentally. This review emphasizes the significant stages in the BCI domain, current problems, and state-of-the-art findings. This article also covers how current results can contribute to new knowledge about BCI, an overview of BCI from its early developments to recent advancements, BCI applications, challenges, and future directions. The authors pointed to unresolved issues and expressed how BCI is valuable for analyzing the human brain. Humans' dependence on machines has led humankind into a new future where BCI can play an essential role in improving this modern world.

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EEG microstate features according to performance on a mental arithmetic task

Cited by 36 publications

References 75 publications

EEG Microstate Correlates of Emotion Dynamics and Stimulation Content during Video Watching

EEG Microstate Correlates of Emotion Dynamics and Stimulation Content during Video Watching

EEG microstates suggest atypical resting‐state network activity in high‐functioning children and adolescents with autism spectrum development

Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities

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