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Zeitschriftenartikel zum Thema „Microstate analysis“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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Wang, Tianjun, Yun-Hsuan Chen, and Mohamad Sawan. "Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study." Bioengineering 10, no. 3 (2023): 281. http://dx.doi.org/10.3390/bioengineering10030281.

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Motor imagery-based brain–computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfactory. Electroencephalography (EEG) signals can be represented by a sequence of a limited number of topographies (microstates). To explore the dynamic brain activation patterns, we conducted EEG microstate and microstate-specific functional connectivity analyses on EEG data under motor imagery (MI),

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Shaw, Saurabh Bhaskar, Kiret Dhindsa, James P. Reilly, and Suzanna Becker. "Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics." Neural Computation 31, no. 11 (2019): 2177–211. http://dx.doi.org/10.1162/neco_a_01229.

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The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory,

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Fu, Yunfa, Jian Chen, and Xin Xiong. "Calculation and Analysis of Microstate Related to Variation in Executed and Imagined Movement of Force of Hand Clenching." Computational Intelligence and Neuroscience 2018 (August 27, 2018): 1–15. http://dx.doi.org/10.1155/2018/9270685.

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Objective. In order to investigate electroencephalogram (EEG) instantaneous activity states related to executed and imagined movement of force of hand clenching (grip force: 4 kg, 10 kg, and 16 kg), we utilized a microstate analysis in which the spatial topographic map of EEG behaves in a certain number of discrete and stable global brain states. Approach. Twenty subjects participated in EEG collection; the global field power of EEG and its local maximum were calculated and then clustered using cross validation and statistics; the 4 parameters of each microstate (duration, occurrence, time cov

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Khazaei, Mohammad, Khadijeh Raeisi, Pierpaolo Croce, et al. "Characterization of the Functional Dynamics in the Neonatal Brain during REM and NREM Sleep States by means of Microstate Analysis." Brain Topography 34, no. 5 (2021): 555–67. http://dx.doi.org/10.1007/s10548-021-00861-1.

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AbstractNeonates spend most of their life sleeping. During sleep, their brain experiences fast changes in its functional organization. Microstate analysis permits to capture the rapid dynamical changes occurring in the functional organization of the brain by representing the changing spatio-temporal features of the electroencephalogram (EEG) as a sequence of short-lasting scalp topographies—the microstates. In this study, we modeled the ongoing neonatal EEG into sequences of a limited number of microstates and investigated whether the extracted microstate features are altered in REM and NREM s

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Cui, Yujie, Songyun Xie, Yingxin Fu, and Xinzhou Xie. "Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis." Brain Sciences 13, no. 9 (2023): 1288. http://dx.doi.org/10.3390/brainsci13091288.

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Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain–computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function. In this paper, four EEG microstates (MS1, MS2, MS3, MS4) were used in the analysis of the differences in the subjects’ MI-BCI performance, and the four microstate feature parameters (the

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Gu, Feng, Anmin Gong, Yi Qu, et al. "Research on Top Archer’s EEG Microstates and Source Analysis in Different States." Brain Sciences 12, no. 8 (2022): 1017. http://dx.doi.org/10.3390/brainsci12081017.

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The electroencephalograph (EEG) microstate is a method used to describe the characteristics of the EEG signal through the brain scalp electrode potential’s spatial distribution; as such, it reflects the changes in the brain’s functional state. The EEGs of 13 elite archers from China’s national archery team and 13 expert archers from China’s provincial archery team were recorded under the alpha rhythm during the resting state (with closed eyes) and during archery aiming. By analyzing the differences between the EEG microstate parameters and the correlation between these parameters with archery

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Shi, Wen, Yamin Li, Zhian Liu, et al. "Non-Canonical Microstate Becomes Salient in High Density EEG During Propofol-Induced Altered States of Consciousness." International Journal of Neural Systems 30, no. 02 (2020): 2050005. http://dx.doi.org/10.1142/s0129065720500057.

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Dynamically assessing the level of consciousness is still challenging during anesthesia. With the help of Electroencephalography (EEG), the human brain electric activity can be noninvasively measured at high temporal resolution. Several typical quasi-stable states are introduced to represent the oscillation of the global scalp electric field. These so-called microstates reflect spatiotemporal dynamics of coherent neural activities and capture the switch of brain states within the millisecond range. In this study, the microstates of high-density EEG were extracted and investigated during propof

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Keihani, Ahmadreza, Seyed Saman Sajadi, Mahsa Hasani, and Fabio Ferrarelli. "Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis." Brain Sciences 12, no. 11 (2022): 1497. http://dx.doi.org/10.3390/brainsci12111497.

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Resting-state electroencephalography (EEG) microstates reflect sub-second, quasi-stable states of brain activity. Several studies have reported alterations of microstate features in patients with schizophrenia (SZ). Based on these findings, it has been suggested that microstates may represent neurophysiological biomarkers for the classification of SZ. To explore this possibility, machine learning approaches can be employed. Bayesian optimization is a machine learning approach that selects the best-fitted machine learning model with tuned hyperparameters from existing models to improve the clas

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Wan, Wang, Zhongze Gu, Chung-Kang Peng, and Xingran Cui. "Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia." Brain Sciences 14, no. 5 (2024): 487. http://dx.doi.org/10.3390/brainsci14050487.

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Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal patterns of EEG can be described through microstate analysis, which provides a discrete approximation of the continuous electric field patterns generated by the brain cortex. Here, we propose a novel microstate spatiotemporal dynamic indicator, termed the microstate sequence non-randomness index (MSNRI). The essence of the method lies in initially generating a sequence of microstate transition patterns through state space

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Pacchioni, Federico, Giacomo Germagnoli, Marta Calbi, et al. "Navigating the Complexity of Psychotic Disorders: A Systematic Review of EEG Microstates and Machine Learning." BioMedInformatics 5, no. 1 (2025): 8. https://doi.org/10.3390/biomedinformatics5010008.

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EEG microstates are brief, stable topographical configurations of brain activity that provide insights into alterations in brain function and connectivity. Anomalies in microstates are associated with different neuropsychiatric conditions, especially schizophrenia. Recent advances in both EEG techniques and machine learning point to the potential role of microstates as diagnostic markers for psychotic disorders. This systematic review aims to gather current knowledge on machine learning applied to EEG microstate analysis in psychotic disorders. Following PRISMA guidelines, we searched Scopus,

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Bai, Yicai, Minchang Yu, and Yingjie Li. "Dynamic Neural Patterns of Human Emotions in Virtual Reality: Insights from EEG Microstate Analysis." Brain Sciences 14, no. 2 (2024): 113. http://dx.doi.org/10.3390/brainsci14020113.

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Emotions play a crucial role in human life and affect mental health. Understanding the neural patterns associated with emotions is essential. Previous studies carried out some exploration of the neural features of emotions, but most have designed experiments in two-dimensional (2D) environments, which differs from real-life scenarios. To create a more real environment, this study investigated emotion-related brain activity using electroencephalography (EEG) microstate analysis in a virtual reality (VR) environment. We recruited 42 healthy volunteers to participate in our study. We explored the

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Kim, Kyungwon, Nguyen Thanh Duc, Min Choi, and Boreom Lee. "EEG microstate features for schizophrenia classification." PLOS ONE 16, no. 5 (2021): e0251842. http://dx.doi.org/10.1371/journal.pone.0251842.

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Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features,

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Tayade, Prashant, Simran Kaur, Suriya Prakash Muthukrishnan, Ratna Sharma, and Gaurav Saini. "EEG microstates in resting condition in young indians." Indian Journal of Physiology and Pharmacology 66 (October 10, 2022): 175–80. http://dx.doi.org/10.25259/ijpp_44_2022.

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Objectives: The map topography analysis gives an idea of temporal dynamics of electric fields, which is reference independent, making the results unambiguous. These topographic maps remain stable for 80 to 100 milliseconds, abruptly shifting to a new topographic map configuration and remains stable in that state are called the ‘functional microstates’ as described by Lehmann et al (1987). There has been no study done in the resting state eye closed and eye open conditions showing the microstate maps in healthy Indian subjects in resting eyes open and resting eyes closed condition using 128 cha

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Erbil, Nurhan, and Gopikrishna Deshpande. "Scale-Free Dynamics of Resting-State fMRI Microstates." Fractal and Fractional 9, no. 2 (2025): 112. https://doi.org/10.3390/fractalfract9020112.

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The functional significance of RSNs is examined via simultaneous EEG-fMRI studies on the basis of the relation of RSNs with different frequency bands of EEG and EEG-based microstate analysis. In this study, we try to identify RSNs from microstates of cortical surface maps of the BOLD signal. In addition, the scale-free dynamics of these map sequences were also examined. The structural and resting state functional MRI images were acquired on a 3T scanner with three different fMRI acquisition protocols from seven subjects. Microstate segmentations from EEG, fMRI, and simulated data were evaluate

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Jabès, Adeline, Giuliana Klencklen, Paolo Ruggeri, Christoph M. Michel, Pamela Banta Lavenex, and Pierre Lavenex. "Resting‐State EEG Microstates Parallel Age‐Related Differences in Allocentric Spatial Working Memory Performance." Brain Topography 34, no. 4 (2021): 442–60. http://dx.doi.org/10.1007/s10548-021-00835-3.

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AbstractAlterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds.

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Ruiz, Pablo, Raquel Tinoco-Egas, and Carlos Cevallos. "Neural States in Tourism Travel Videos." Proceedings 71, no. 1 (2020): 6. http://dx.doi.org/10.3390/iecbs-08465.

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In marketing, there are many methods to relate reactions to products to customer preference. Current electroencephalography (EEG) signal analysis in the neuromarketing field focuses mainly on correlations between selected electrodes and hemisphere-based analysis on single scalp measures. The present study shows microstate analysis of brain EEG signals in goal-oriented videos. We measured a 16 channel EEG with an Emotiv EPOC+ device. We used two oriented videos from the Ecuadorian Government to publicize Ecuador as a tourist destination. We used a Topographic Atomize and Agglomerate Hierarchica

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Liang, Andi, Shanguang Zhao, Jing Song, et al. "Treatment Effect of Exercise Intervention for Female College Students with Depression: Analysis of Electroencephalogram Microstates and Power Spectrum." Sustainability 13, no. 12 (2021): 6822. http://dx.doi.org/10.3390/su13126822.

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This paper aims to assess the effect of exercise intervention on the improvement of college students with depression and to explore the change characteristics of microstates and the power spectrum in their resting-state electroencephalogram (EEG). Forty female college students with moderate depression were screened according to the Beck Depression Inventory-II (BDI-II) and Depression Self-Rating Scale (SDS) scores, and half of them received an exercise intervention for 18 weeks. The study utilized an EEG to define the resting-state networks, and the scores of all the participants were tracked

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Chen, Chen, Jinying Han, Shuang Zheng, et al. "Dynamic Changes of Brain Activity in Different Responsive Groups of Patients with Prolonged Disorders of Consciousness." Brain Sciences 13, no. 1 (2022): 5. http://dx.doi.org/10.3390/brainsci13010005.

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As medical technology continues to improve, many patients diagnosed with brain injury survive after treatments but are still in a coma. Further, multiple clinical studies have demonstrated recovery of consciousness after transcranial direct current stimulation. To identify possible neurophysiological mechanisms underlying disorders of consciousness (DOCs) improvement, we examined the changes in multiple resting-state EEG microstate parameters after high-definition transcranial direct current stimulation (HD-tDCS). Because the left dorsolateral prefrontal cortex is closely related to consciousn

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Chang, Qi, Cancheng Li, Jicong Zhang, and Chuanyue Wang. "Dynamic brain functional network based on EEG microstate during sensory gating in schizophrenia." Journal of Neural Engineering 19, no. 2 (2022): 026007. http://dx.doi.org/10.1088/1741-2552/ac5266.

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Abstract Objective. Cognitive impairment is one of the core symptoms of schizophrenia, with an emphasis on dysfunctional information processing. Sensory gating deficits have consistently been reported in schizophrenia, but the underlying physiological mechanism is not well-understood. We report the discovery and characterization of P50 dynamic brain connections based on microstate analysis. Approach. We identify five main microstates associated with the P50 response and the difference between the first and second click presentation (S1-S2-P50) in first-episode schizophrenia (FESZ) patients, ul

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Hua, Wangchun, and Yingjie Li. "Electroencephalography Based Microstate Functional Connectivity Analysis in Emotional Cognitive Reappraisal Combined with Happy Music." Brain Sciences 13, no. 4 (2023): 554. http://dx.doi.org/10.3390/brainsci13040554.

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Currently, research mainly focuses on the effects of happy music on the subjective assessment of cognitive reappraisal, but relevant results of the neural mechanism are lacking. By analysing the functional connectivity of microstates based on electroencephalography (EEG), we investigated the effect of cognitive reappraisal combined with happy music on emotional regulation and the dynamic characteristics of brain functional activities. A total of 52 healthy college students were divided into music group and control group. EEG data and behavioural scores were collected during an experiment of co

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Wei, Zhen, Hongwei Li, Lin Ma, and Haifeng Li. "Emotion recognition based on microstate analysis from temporal and spatial patterns of electroencephalogram." Frontiers in Neuroscience 18 (March 14, 2024). http://dx.doi.org/10.3389/fnins.2024.1355512.

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IntroductionRecently, the microstate analysis method has been widely used to investigate the temporal and spatial dynamics of electroencephalogram (EEG) signals. However, most studies have focused on EEG at resting state, and few use microstate analysis to study emotional EEG. This paper aims to investigate the temporal and spatial patterns of EEG in emotional states, and the specific neurophysiological significance of microstates during the emotion cognitive process, and further explore the feasibility and effectiveness of applying the microstate analysis to emotion recognition.MethodsWe prop

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Artoni, Fiorenzo, and Christoph M. Michel. "How does Independent Component Analysis Preprocessing Affect EEG Microstates?" Brain Topography 38, no. 2 (2025). https://doi.org/10.1007/s10548-024-01098-4.

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Abstract Over recent years, electroencephalographic (EEG) microstates have been increasingly used to investigate, at a millisecond scale, the temporal dynamics of large-scale brain networks. By studying their topography and chronological sequence, microstates research has contributed to the understanding of the brain’s functional organization at rest and its alteration in neurological or mental disorders. Artifact removal strategies, which differ from study to study, may alter microstates topographies and features, possibly reducing the generalizability and comparability of results across rese

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Xiong, Xin, Yuyan Ren, Shenghan Gao, et al. "EEG microstate in obstructive sleep apnea patients." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-95749-2.

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AbstractObstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA)

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Ricci, Lorenzo, Pierpaolo Croce, Patrizia Pulitano, et al. "Levetiracetam Modulates EEG Microstates in Temporal Lobe Epilepsy." Brain Topography, September 13, 2022. http://dx.doi.org/10.1007/s10548-022-00911-2.

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AbstractTo determine the effects of Levetiracetam (LEV) therapy using EEG microstates analysis in a population of newly diagnosed Temporal Lobe Epilepsy (TLE) patients. We hypothesized that the impact of LEV therapy on the electrical activity of the brain can be globally explored using EEG microstates. Twenty-seven patients with TLE were examined. We performed resting-state microstate EEG analysis and compared microstate metrics between the EEG performed at baseline (EEGpre) and after 3 months of LEV therapy (EEGpost). The microstates A, B, C and D emerged as the most stable. LEV induced a red

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Xiong, Xin, Jiannan Feng, Yaru Zhang, et al. "Improved HHT-microstate analysis of EEG in nicotine addicts." Frontiers in Neuroscience 17 (May 24, 2023). http://dx.doi.org/10.3389/fnins.2023.1174399.

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BackgroundSubstance addiction is a chronic disease which causes great harm to modern society and individuals. At present, many studies have applied EEG analysis methods to the substance addiction detection and treatment. As a tool to describe the spatio-temporal dynamic characteristics of large-scale electrophysiological data, EEG microstate analysis has been widely used, which is an effective method to study the relationship between EEG electrodynamics and cognition or disease.MethodsTo study the difference of EEG microstate parameters of nicotine addicts at each frequency band, we combine an

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Chen, Jing, Haifeng Li, Lin Ma, Hongjian Bo, Frank Soong, and Yaohui Shi. "Dual-Threshold-Based Microstate Analysis on Characterizing Temporal Dynamics of Affective Process and Emotion Recognition From EEG Signals." Frontiers in Neuroscience 15 (July 14, 2021). http://dx.doi.org/10.3389/fnins.2021.689791.

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Recently, emotion classification from electroencephalogram (EEG) data has attracted much attention. As EEG is an unsteady and rapidly changing voltage signal, the features extracted from EEG usually change dramatically, whereas emotion states change gradually. Most existing feature extraction approaches do not consider these differences between EEG and emotion. Microstate analysis could capture important spatio-temporal properties of EEG signals. At the same time, it could reduce the fast-changing EEG signals to a sequence of prototypical topographical maps. While microstate analysis has been

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Nagabhushan Kalburgi, Sahana, Tobias Kleinert, Delara Aryan, Kyle Nash, Bastian Schiller, and Thomas Koenig. "MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis." Brain Topography, September 11, 2023. http://dx.doi.org/10.1007/s10548-023-01003-5.

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AbstractMicrostate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and

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Wu, Xiaotian, Yanli Liu, Jiajun Che, et al. "Unveiling neural activity changes in mild cognitive impairment using microstate analysis and machine learning." Journal of Alzheimer’s Disease, January 8, 2025. https://doi.org/10.1177/13872877241305961.

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Background Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understanding the neural correlates of MCI is crucial for elucidating its pathophysiology and developing effective interventions. Electroencephalogram (EEG) microstates, reflecting brain activity changes, have shown promise in MCI research. However, current approaches often lack comprehensive characterization of the complex neural dynamics associated with MCI. Objective This study aims to investigate neurophysiological changes associated with MCI using a co

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Zerna, Josephine, Alexander Strobel, and Christoph Scheffel. "EEG microstate analysis of emotion regulation reveals no sequential processing of valence and emotional arousal." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-00731-7.

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AbstractIn 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, cognitiv

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Zhao, Qinglin, Kunbo Cui, Lixin Zhang, et al. "Identifying and predicting EEG microstates with sequence-to-sequence deep learning models for online applications." Journal of Neural Engineering, June 6, 2025. https://doi.org/10.1088/1741-2552/ade1f8.

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Abstract Objective: Electroencephalographic (EEG) microstates, as a non-invasive and high-temporal-resolution tool for analyzing time-space features of brain activity, have been validated and applied in various research domains. However, current methods for EEG microstate analysis rely on clustering algorithms, which require large-scale offline computations to obtain microstate labels and cluster centers. This offline approach is no longer sufficient for applications in cross-subject, cross-dataset, and multi-task scenarios. Approach: To address these limitations, we propose, for the first tim

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Wang, Fanglan, Khamlesh Hujjaree, and Xiaoping Wang. "Electroencephalographic Microstates in Schizophrenia and Bipolar Disorder." Frontiers in Psychiatry 12 (February 26, 2021). http://dx.doi.org/10.3389/fpsyt.2021.638722.

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Schizophrenia (SCH) and bipolar disorder (BD) are characterized by many types of symptoms, damaged cognitive function, and abnormal brain connections. The microstates are considered to be the cornerstones of the mental states shown in EEG data. In our study, we investigated the use of microstates as biomarkers to distinguish patients with bipolar disorder from those with schizophrenia by analyzing EEG data measured in an eyes-closed resting state. The purpose of this article is to provide an electron directional physiological explanation for the observed brain dysfunction of schizophrenia and

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Li, Wenbin, Shan Cheng, Jing Dai, and Yaoming Chang. "Effects of Mental Workload Manipulation on Electroencephalography Spectrum Oscillation and Microstates in Multitasking Environments." Brain and Behavior 15, no. 1 (2025). https://doi.org/10.1002/brb3.70216.

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ABSTRACTIntroductionMultitasking during flights leads to a high mental workload, which is detrimental for maintaining task performance. Electroencephalography (EEG) power spectral analysis based on frequency‐band oscillations and microstate analysis based on global brain network activation can be used to evaluate mental workload. This study explored the effects of a high mental workload during simulated flight multitasking on EEG frequency‐band power and microstate parameters.MethodsThirty‐six participants performed multitasking with low and high mental workloads after 4 consecutive days of tr

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Truong, Nghi Cong Dung, Xinlong Wang, and Hanli Liu. "Temporal and spectral analyses of EEG microstate reveals neural effects of transcranial photobiomodulation on the resting brain." Frontiers in Neuroscience 17 (October 17, 2023). http://dx.doi.org/10.3389/fnins.2023.1247290.

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IntroductionThe quantification of electroencephalography (EEG) microstates is an effective method for analyzing synchronous neural firing and assessing the temporal dynamics of the resting state of the human brain. Transcranial photobiomodulation (tPBM) is a safe and effective modality to improve human cognition. However, it is unclear how prefrontal tPBM neuromodulates EEG microstates both temporally and spectrally.Methods64-channel EEG was recorded from 45 healthy subjects in both 8-min active and sham tPBM sessions, using a 1064-nm laser applied to the right forehead of the subjects. After

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Jaltare, Ketan Prafull, and Diana M. Torta. "Experimentally induced central sensitization is accompanied by alterations in electroencephalographical microstate parameters." Pain, February 18, 2025. https://doi.org/10.1097/j.pain.0000000000003546.

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Abstract Pain perception is a dynamic and time-varying phenomenon. The high temporal resolution of electroencephalography (EEG) can be leveraged to gain insight into its cortical dynamics. Electroencephalography microstate analysis is a novel technique that parses multichannel EEG signals into a limited number of quasi-stable topographies (microstates) that have a meaningful temporal structure and have been linked to the activity of resting state networks. In recent years, several studies have investigated alterations in EEG microstate parameters associated with acute and chronic pain states,

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Yan, Yibing, Manman Gao, Zhi Geng та ін. "Abnormal EEG microstates in Alzheimer’s disease: predictors of β-amyloid deposition degree and disease classification". GeroScience, 10 травня 2024. http://dx.doi.org/10.1007/s11357-024-01181-5.

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AbstractElectroencephalography (EEG) microstates are used to study cognitive processes and brain disease-related changes. However, dysfunctional patterns of microstate dynamics in Alzheimer's disease (AD) remain uncertain. To investigate microstate changes in AD using EEG and assess their association with cognitive function and pathological changes in cerebrospinal fluid (CSF). We enrolled 56 patients with AD and 38 age- and sex-matched healthy controls (HC). All participants underwent various neuropsychological assessments and resting-state EEG recordings. Patients with AD also underwent CSF

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Zhang, Chi, Xiaoguang Wang, Zhiwei Ding, et al. "Study on tinnitus-related electroencephalogram microstates in patients with vestibular schwannomas." Frontiers in Neuroscience 17 (April 6, 2023). http://dx.doi.org/10.3389/fnins.2023.1159019.

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Tinnitus is closely associated with cognition functioning. In order to clarify the central reorganization of tinnitus in patients with vestibular schwannoma (VS), this study explored the aberrant dynamics of electroencephalogram (EEG) microstates and their correlations with tinnitus features in VS patients. Clinical and EEG data were collected from 98 VS patients, including 76 with tinnitus and 22 without tinnitus. Microstates were clustered into four categories. Our EEG microstate analysis revealed that VS patients with tinnitus exhibited an increased frequency of microstate C compared to tho

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Ling, Shihai, Lingyan Du, Xi Tan, Guozhi Tang, Yue Che, and Shirui Song. "EEG Microstate Dynamics during Different Physiological Developmental Stages and the Effects of Medication in Schizophrenia." Journal of Integrative Neuroscience 24, no. 3 (2025). https://doi.org/10.31083/jin27059.

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Background: Schizophrenia (SCZ) is associated with abnormal neural activities and brain connectivity. Electroencephalography (EEG) microstate is a voltage topographical representation of temporary brain network activations. Most research on EEG microstates in SCZ has focused on differences between patients and healthy controls (HC). However, changes in EEG microstates among SCZ patients across various stages of physiological and cognitive development have not been thoroughly assessed. Consequently, we stratified patients with SCZ into four age-specific cohorts (20–29 years (brain maturation),

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Ling, Yi, Xinrui Wen, Jianghui Tang, et al. "Effect of topographic comparison of electroencephalographic microstates on the diagnosis and prognosis prediction of patients with prolonged disorders of consciousness." CNS Neuroscience & Therapeutics, September 7, 2023. http://dx.doi.org/10.1111/cns.14421.

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AbstractAimsThe electroencephalography (EEG) microstates are indicative of fundamental information processing mechanisms, which are severely damaged in patients with prolonged disorders of consciousness (pDoC). We aimed to improve the topographic analysis of EEG microstates and explore indicators available for diagnosis and prognosis prediction of patients with pDoC, which were still lacking.MethodsWe conducted EEG recordings on 59 patients with pDoC and 32 healthy controls. We refined the microstate method to accurately estimate topographical differences, and then classify and forecast the pr

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Das, Sushmit, Reza Zomorrodi, Peter G. Enticott, et al. "Resting state electroencephalography microstates in autism spectrum disorder: A mini-review." Frontiers in Psychiatry 13 (December 1, 2022). http://dx.doi.org/10.3389/fpsyt.2022.988939.

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Atypical spatial organization and temporal characteristics, found via resting state electroencephalography (EEG) microstate analysis, have been associated with psychiatric disorders but these temporal and spatial parameters are less known in autism spectrum disorder (ASD). EEG microstates reflect a short time period of stable scalp potential topography. These canonical microstates (i.e., A, B, C, and D) and more are identified by their unique topographic map, mean duration, fraction of time covered, frequency of occurrence and global explained variance percentage; a measure of how well topogra

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Wang, Yao, Jianing Wang, and Chong Lu. "Neural mechanisms of spatial navigation in ASD and TD children: insights from EEG microstate and functional connectivity analysis." Frontiers in Psychiatry 16 (April 4, 2025). https://doi.org/10.3389/fpsyt.2025.1552233.

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IntroductionAutism Spectrum Disorder (ASD) is associated with atypical neural dynamics, affecting spatial navigation and information integration. EEG microstates and functional connectivity (FC) are useful tools for investigating these differences. This study examines alterations in EEG microstates and theta-band FC during map-reading tasks in children with ASD (n = 12) compared to typically developing (TD) peers (n = 12), aiming to uncover neural mechanisms underlying spatial processing deficits in ASD.MethodsEEG data were collected from children with ASD (n = 12) and TD controls (n = 12) age

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Sun, Qiaoling, Linlin Zhao, and Liwen Tan. "Abnormalities of Electroencephalography Microstates in Drug-Naïve, First-Episode Schizophrenia." Frontiers in Psychiatry 13 (March 14, 2022). http://dx.doi.org/10.3389/fpsyt.2022.853602.

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ObjectiveMicrostate analysis is a powerful tool to probe the brain functions, and changes in microstates under electroencephalography (EEG) have been repeatedly reported in patients with schizophrenia. This study aimed to investigate the dynamics of EEG microstates in drug-naïve, first-episode schizophrenia (FE-SCH) and to test the relationship between EEG microstates and clinical symptoms.MethodsResting-state EEG were recorded for 23 patients with FE-SCH and 23 healthy controls using a 64-channel cap. Three parameters, i.e., contribution, duration, and occurrence, of the four microstate class

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Chen, Jingxin, Yufeng Ke, Guangjian Ni, Shuang Liu, and Dong Ming. "Evidence for modulation of EEG microstates by mental workload levels and task types." Human Brain Mapping, December 5, 2023. http://dx.doi.org/10.1002/hbm.26552.

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AbstractElectroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large‐scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulat

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Li, Yansong, Guoliang Chen, Jing Lv, et al. "Abnormalities in resting-state EEG microstates are a vulnerability marker of migraine." Journal of Headache and Pain 23, no. 1 (2022). http://dx.doi.org/10.1186/s10194-022-01414-y.

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Abstract Background Resting-state EEG microstates are thought to reflect brief activations of several interacting components of resting-state brain networks. Surprisingly, we still know little about the role of these microstates in migraine. In the present study, we attempted to address this issue by examining EEG microstates in patients with migraine without aura (MwoA) during the interictal period and comparing them with those of a group of healthy controls (HC). Methods Resting-state EEG was recorded in 61 MwoA patients (50 females) and 66 HC (50 females). Microstate parameters were compare

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Bagdasarov, Armen, Denis Brunet, Christoph M. Michel, and Michael S. Gaffrey. "Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability." Brain Topography, March 2, 2024. http://dx.doi.org/10.1007/s10548-024-01043-5.

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AbstractMicrostate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy – a critical period of rapid brain development and plasticity – microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong ps

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Osumi, Michihiro, Masahiko Sumitani, Katsuyuki Iwatsuki, et al. "Resting-state Electroencephalography Microstates Correlate with Pain Intensity in Patients with Complex Regional Pain Syndrome." Clinical EEG and Neuroscience, October 16, 2023. http://dx.doi.org/10.1177/15500594231204174.

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Objective: Severe pain and other symptoms in complex regional pain syndrome (CRPS), such as allodynia and hyperalgesia, are associated with abnormal resting-state brain network activity. No studies to date have examined resting-state brain networks in CRPS patients using electroencephalography (EEG), which can clarify the temporal dynamics of brain networks. Methods: We conducted microstate analysis using resting-state EEG signals to prospectively reveal direct correlations with pain intensity in CRPS patients (n = 17). Five microstate topographies were fitted back to individual CRPS patients’

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Kleinert, Tobias, Kyle Nash, Thomas Koenig, and Edmund Wascher. "Normative Intercorrelations Between EEG Microstate Characteristics." Brain Topography, July 14, 2023. http://dx.doi.org/10.1007/s10548-023-00988-3.

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AbstractEEG microstates are brief, recurring periods of stable brain activity that reflect the activation of large-scale neural networks. The temporal characteristics of these microstates, including their average duration, number of occurrences, and percentage contribution have been shown to serve as biomarkers of mental and neurological disorders. However, little is known about how microstate characteristics of prototypical network types relate to each other. Normative intercorrelations among these parameters are necessary to help researchers better understand the functions and interactions o

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Zarka, D., C. Cevallos, P. Ruiz, et al. "Electroencephalography microstates highlight specific mindfulness traits." European Journal of Neuroscience, January 14, 2024. http://dx.doi.org/10.1111/ejn.16247.

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AbstractThe present study aimed to investigate the spontaneous dynamics of large‐scale brain networks underlying mindfulness as a dispositional trait, through resting‐state electroencephalography (EEG) microstates analysis. Eighteen participants had attended a standardized mindfulness‐based stress reduction training (MBSR), and 18 matched waitlist individuals (CTRL) were recorded at rest while they were passively exposed to auditory stimuli. Participants' mindfulness traits were assessed with the Five Facet Mindfulness Questionnaire (FFMQ). To further explore the relationship between microstat

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Zhu, Min, and Qin Gong. "EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training." Frontiers in Neuroscience 17 (December 11, 2023). http://dx.doi.org/10.3389/fnins.2023.1254423.

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Tailor-made notched music training (TMNMT) is a promising therapy for tinnitus. Residual inhibition (RI) is one of the few interventions that can temporarily inhibit tinnitus, which is a useful technique that can be applied to tinnitus research and explore tinnitus mechanisms. In this study, RI effect of TMNMT in tinnitus was investigated mainly using behavioral tests, EEG spectral and microstate analysis. To our knowledge, this study is the first to investigate RI effect of TMNMT. A total of 44 participants with tinnitus were divided into TMNMT group (22 participants; ECnm, NMnm, RInm represe

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Zhu, Chaofeng, Jinying Zhang, Shenzhi Fang, et al. "Intrinsic brain activity differences in drug-resistant epilepsy and well-controlled epilepsy patients: an EEG microstate analysis." Therapeutic Advances in Neurological Disorders 17 (January 2024). https://doi.org/10.1177/17562864241307846.

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Background: Drug-resistant epilepsy (DRE) patients exhibit aberrant large-scale brain networks. Objective: The purpose of investigation is to explore the differences in resting-state electroencephalogram (EEG) microstates between patients with DRE and well-controlled (W-C) epilepsy. Design: Retrospective study. Methods: Clinical data of epilepsy patients treated at the Epilepsy Center of Fujian Medical University Union Hospital from January 2020 to May 2023 were collected for a minimum follow-up period of 2 years. Participants meeting inclusion and exclusion criteria were categorized into two

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Yu, Fang, Yanzhe Gao, Fenglian Li, et al. "Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness." Frontiers in Neuroscience 17 (October 2, 2023). http://dx.doi.org/10.3389/fnins.2023.1257511.

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IntroductionIschemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology.MethodsIn our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal featu

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