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Relevant bibliographies by topics / EEG MICROSTATES

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Academic literature on the topic 'EEG MICROSTATES'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "EEG MICROSTATES"

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Kong, Wanzeng, Luyun Wang, Jianhai Zhang, Qibin Zhao, and Junfeng Sun. "The Dynamic EEG Microstates in Mental Rotation." Sensors 18, no. 9 (September 3, 2018): 2920. http://dx.doi.org/10.3390/s18092920.

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Mental rotation is generally analyzed based on event-related potential (ERP) in a time domain with several characteristic electrodes, but neglects the whole spatial-temporal brain pattern in the cognitive process which may reflect the underlying cognitive mechanism. In this paper, we mainly proposed an approach based on microstates to examine the encoding of mental rotation from the spatial-temporal changes of EEG signals. In particular, we collected EEG data from 11 healthy subjects in a mental rotation cognitive task using 12 different stimulus pictures representing left and right hands at various rotational angles. We applied the microstate method to investigate the microstates conveyed by the event-related potential extracted from EEG data during mental rotation, and obtained four microstate modes (referred to as modes A, B, C, D, respectively). Subsequently, we defined several measures, including microstate sequences, topographical map, hemispheric lateralization, and duration of microstate, to characterize the dynamics of microstates during mental rotation. We observed that (1) the microstates sequence had a specified progressing mode, i.e., A → B → A ; (2) the activation of the right parietal occipital region was stronger than that of the left parietal occipital region according to the hemispheric lateralization of the microstates mode A; and (3) the duration of the second microstates mode A showed the shorter duration in the vertical stimuli, named “angle effect”.

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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 (November 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, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.

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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 coverage, and amplitude) were calculated from the clustering results and statistically analyzed by analysis of variance (ANOVA); finally, the relationship between the microstate and frequency band was analyzed. Main Results. The experimental results showed that all microstates related to executed and imagined grip force tasks were clustered into 3 microstate classes (A, B, and C); these microstates generally transitioned from A to B and then from B to C. With the increase of the target value of executed and imagined grip force, the duration and time coverage of microstate B gradually decreased, while these parameters of microstate C gradually increased. The occurrence times of microstate B and C related to executed grip force were significantly more than those related to imagined grip force; furthermore, the amplitudes of these 3 microstates related to executed grip force were significantly greater than those related to imagined grip force. The correlation coefficients between the microstates and the frequency bands indicated that the microstates were correlated to mu rhythm and beta frequency bands, which are consistent with event-related desynchronization/synchronization (ERD/ERS) phenomena of sensorimotor rhythm. Significance. It is expected that this microstate analysis may be used as a new method for observing EEG instantaneous activity patterns related to variation in executed and imagined grip force and also for extracting EEG features related to these tasks. This study may lay a foundation for the application of executed and imagined grip force training for rehabilitation of hand movement disorders in patients with stroke in the future.

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Shi, Wen, Yamin Li, Zhian Liu, Jing Li, Qiang Wang, Xiangguo Yan, and Gang Wang. "Non-Canonical Microstate Becomes Salient in High Density EEG During Propofol-Induced Altered States of Consciousness." International Journal of Neural Systems 30, no. 02 (January 23, 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 propofol-induced transition of consciousness. To analyze microstates on the frequency domain, a novel microstate-wise spectral analysis was proposed by the means of multivariate empirical mode decomposition and Hilbert–Huang transform. During the transition of consciousness, a map with a posterior central maximum denoted as microstate F appeared and became salient. The current results indicated that the coverage, occurrence, and power of microstate F significantly increased in moderate sedation. The results also demonstrated that the transition of brain state from rest to sedation was accompanied by significant increase in mean energy of all frequency bands in microstate F. Combined with studies on the possible cortical sources of microstates, the findings reveal that non-canonical microstate F is highly associated with propofol-induced altered states of consciousness. The results may also support the inference that this distinct topography can be derived from canonical microstate C (anterior-posterior orientation). Finally, this study further develops pertinent methodology and extends possible applications of the EEG microstate during propofol-induced anesthesia.

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Lehmann, Dietrich, Roberto Pascual-Marqui, and Christoph Michel. "EEG microstates." Scholarpedia 4, no. 3 (2009): 7632. http://dx.doi.org/10.4249/scholarpedia.7632.

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Khazaei, Mohammad, Khadijeh Raeisi, Pierpaolo Croce, Gabriella Tamburro, Anton Tokariev, Sampsa Vanhatalo, Filippo Zappasodi, and Silvia Comani. "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 (July 13, 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 sleep (usually known as active and quiet sleep states—AS and QS—in the newborn) and depend on the EEG frequency band. 19-channel EEG recordings from 60 full-term healthy infants were analyzed using a modified version of the k-means clustering algorithm. The results show that ~ 70% of the variance in the datasets can be described using 7 dominant microstate templates. The mean duration and mean occurrence of the dominant microstates were significantly different in the two sleep states. Microstate syntax analysis demonstrated that the microstate sequences characterizing AS and QS had specific non-casual structures that differed in the two sleep states. Microstate analysis of the neonatal EEG in specific frequency bands showed a clear dependence of the explained variance on frequency. Overall, our findings demonstrate that (1) the spatio-temporal dynamics of the neonatal EEG can be described by non-casual sequences of a limited number of microstate templates; (2) the brain dynamics described by these microstate templates depends on frequency; (3) the features of the microstate sequences can well differentiate the physiological conditions characterizing AS and QS.

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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 (February 21, 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), motor execution (ME), and guided MI (GMI) conditions. By comparing sixteen microstate parameters, the brain activation patterns induced by GMI show more similarities to ME than MI from a microstate perspective. The mean duration and duration of microstate four are proposed as biomarkers to evaluate motor condition. A support vector machine (SVM) classifier trained with microstate parameters achieved average accuracies of 80.27% and 66.30% for ME versus MI and GMI classification, respectively. Further, functional connectivity patterns showed a strong relationship with microstates. Key node analysis shows clear switching of key node distribution between brain areas among different microstates. The neural mechanism of the switching pattern is discussed. While microstate analysis indicates similar brain dynamics between GMI and ME, graph theory-based microstate-specific functional connectivity analysis implies that visual guidance may reduce the functional integration of the brain network during MI. Thus, we proposed that combined MI and GMI for BCI can improve neurorehabilitation effects. The present findings provide insights for understanding the neural mechanism of microstates, the role of visual guidance in MI tasks, and the experimental basis for developing new BCI-aided rehabilitation systems.

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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 channel EEG. So our study aim was to assess the microstates in resting eyes closed and eyes open condition. And to compare the microstate parameters such as mean duration, global explained variance (GEV) and time coverage between eyes closed and eyes open condition. Materials and Methods: A cross-sectional and observational study on 20 indian subjects (Mean age- 26.65 and (SD) - 2.78 years) was done on resting eyes closed and eyes open conditions. After EEG acquisition using 128 channel EEG machine, EEG was preprocessed and microstate analysis using CARTOOL software was performed on the EEG data. Results: After microstate analysis four maps topography were obtained. There was statistically non-significant difference observed in microstate maps between resting eyes closed and resting eyes open condition for the all parameters i.e. mean duration, time coverage and GEV. Conclusion: Our findings suggests that, both eyes closed and eyes open conditions were similar to each other in terms of stability and predominance of Maps.

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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 (April 19, 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. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.

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Tarailis, Povilas, Dovilė Šimkutė, Thomas Koenig, and Inga Griškova-Bulanova. "Relationship between Spatiotemporal Dynamics of the Brain at Rest and Self-Reported Spontaneous Thoughts: An EEG Microstate Approach." Journal of Personalized Medicine 11, no. 11 (November 17, 2021): 1216. http://dx.doi.org/10.3390/jpm11111216.

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Rationale: The resting-state paradigm is frequently applied in electroencephalography (EEG) research; however, it is associated with the inability to control participants’ thoughts. To quantify subjects’ subjective experiences at rest, the Amsterdam Resting-State Questionnaire (ARSQ) was introduced covering ten dimensions of mind wandering. We aimed to estimate associations between subjective experiences and resting-state microstates of EEG. Methods: 5 min resting-state EEG data of 197 subjects was used to evaluate temporal properties of seven microstate classes. Bayesian correlation approach was implemented to assess associations between ARSQ domains assessed after resting and parameters of microstates. Results: Several associations between Comfort, Self and Somatic Awareness domains and temporal properties of neuroelectric microstates were revealed. The positive correlation between Comfort and duration of microstates E showed the strongest evidence (BF10 > 10); remaining correlations showed substantial evidence (10 > BF10 > 3). Conclusion: Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the intrinsic brain activity reflected in microstates.

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Dissertations / Theses on the topic "EEG MICROSTATES"

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Rajkumar, Ravichandran [Verfasser], Irene Akademischer Betreuer] Neuner, and N. Jon [Akademischer Betreuer] [Shah. "Simultaneous trimodal MR/PET/EEG imaging : a study of the attenuation effect of EEG caps on PET images and a comparison of EEG microstates with resting state fMRI and FDG-PET measures / Ravichandran Rajkumar ; Irene Neuner, Nadim Joni Shah." Aachen : Universitätsbibliothek der RWTH Aachen, 2020. http://d-nb.info/1225401666/34.

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Rajkumar, Ravichandran Verfasser], Irene [Akademischer Betreuer] Neuner, and N. Jon [Akademischer Betreuer] [Shah. "Simultaneous trimodal MR/PET/EEG imaging : a study of the attenuation effect of EEG caps on PET images and a comparison of EEG microstates with resting state fMRI and FDG-PET measures / Ravichandran Rajkumar ; Irene Neuner, Nadim Joni Shah." Aachen : Universitätsbibliothek der RWTH Aachen, 2020. http://d-nb.info/1225401666/34.

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Jordánek, Tomáš. "Prostorovo-časová analýza HD-EEG dat u pacientů s neurodegenerativním onemocněním." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442499.

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This master’s thesis deals with diagnostics of prodromal stage of Lewy body disease using microstate analysis. First part of the thesis includes theoretical background which is needed for understanding discussed topics and presented results. This part consists of description of the disease, diagnostic options, electroencephalography, pre-processing of the EEG record and the microstate analysis process. Theoretical background is followed by a practical part of the thesis. In the beginning, there is a chapter about a dataset, used EEG device, and own solution of the pre-processing. Microstate analysis is discussed next, its output parameters were compared between groups with statistical methods. Comparison of the subjects in prodromal stage of Lewy body disease and healthy controls brought significant differences in three parameters of microstates, in rate of unlabelled time frames and also for some counts of transitions between each map or unlabelled sections. Comparison of the subjects in prodromal stage of Lewy body disease and healthy controls brought significant differences in three parameters of microstates, in rate of unlabelled time frames and also for some counts of transitions between each map or unlabelled sections.

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KAUR, ARDAMAN. "HEMISPHERIC ASYMMETRY ANALYSES THROUGH COMPUTATIONAL NEUROSCIENCE MODELS WITH EMPHASIS ON EEG MICROSTATES : EEG-FMRI DATA INTEGRATION APPROACH." Thesis, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18116.

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It is often assumed that there is a direct correlation between the knowledge an individual possesses and that individual's actions. However, many hidden processes influence decisionmaking processes. Asymmetric processing of affective, cognitive, and sensory information has long been one of the fascinating properties of human brain function. Thus, understanding hemispheric asymmetry as one of those hidden processes can bridge the gap between what a person knows and what one decides to do. One widely used technique for analysis of brain asymmetry is Electroencephalography (EEG), whose simplicity, portability, and high temporal resolution enable its usage in a relatively wide-range of real-world environments. Howbeit, it also poses a drawback of less spatial resolution as the localization of an active site is limited to several centimeters. The hemispheric difference between EEG alpha activity over the frontal regions has been termed as frontal EEG asymmetry. This phenomenon was first linked to patterns of emotion processing decades ago. functional Magnetic Resonance Imaging (fMRI) is another technique that provides a unique view of the human brain by detecting changes in blood oxygenation. It poses an advantage of high spatial resolution; however, it possesses a low temporal resolution. The hemispheric dominance in fMRI has been called the laterality index. The laterality index enables one value per subject per contrast as a descriptor for activation pattern based hemispheric dominance. Also, vii simultaneous recordings and analysis of EEG-fMRI techniques, which can counteract the limitations posed by EEG-FMRI, have recently gained attention and can be gauged for effectiveness in the hemispheric asymmetry research. The current thesis aims to corroborate the hemispheric asymmetry research by exploring the resting-state EEG/fMRI hemispheric asymmetry models after simultaneous EEG-fMRI data acquisition. These resting-state models of hemispheric asymmetry in the brain may serve as potential parameters for comprehending the human actions when engaged in any exogenously directed task. Thus, the standard resting frontal alpha EEG hemispheric asymmetry model was first examined before engagement in a Situational awareness (SA) task to vindicate the relationship between pre-task resting asymmetry and SA-task performance. SA is the knowledge of the environment, and maintenance of SA is crucial for optimal performance in the aviation and military domain. Thus, understanding the linkage of the neural mechanisms underlying the pre-task resting frontal alpha asymmetry model with subsequently performed SA-task can improve SA. For this purpose, initially, an SA-task with influence from the Stroop effect was designed and developed, and pre-task resting EEG absolute alpha power and its frontal alpha hemispheric asymmetry were assessed. This study revealed a strong association of SA-task performance measures with resting frontal alpha hemispheric asymmetry. Further, the neural mechanisms underlying pre-SA task resting absolute alpha power and its frontal asymmetry, as assessed through the EEG-informed fMRI approach, significantly influenced the SA-task's neural mechanisms. After examining the relationship between the pre-task resting alpha EEG asymmetry model with subsequently performed SA-task, the association of this standard asymmetry model with affect and approach/withdrawal measures of an individual was gauged. The purpose of this viii study was to understand the significance of real-time standalone recordings of pre-task resting alpha EEG asymmetry in terms of its connectedness with measures of positive/negative affect and approach/withdrawal behavior. Further, to strengthen the findings, the mapping between pretask resting alpha EEG asymmetry model and fMRI through EEG-informed fMRI analysis was explored. For this purpose, initially, the robust correlation of standard resting frontal alpha asymmetry with affect and approach/withdrawal measures was carried out. Next, the neural underpinnings and Hemodynamic Lateralization Index, HLI (based on these neural underpinnings) for standard resting frontal alpha asymmetry were assessed. The results yielded no significant relationship between the standard resting frontal asymmetry and its HLI with any psychological measures. This ambivalence on the validity of standard resting frontal alpha asymmetry in terms of its association with affect and approach/withdrawal psychological measures motivated us towards the estimation of a novel microstate-based frontal alpha asymmetry model and assessment of this model’s linkage with positive/negative affect and approach/withdrawal measures. The microstates represent global electrical brain activity on the scalp that remains semi-stable for brief transient periods. The utilization of microstates was based on the evidence that supported the importance of stable EEG patterns in bringing forth the interrelation of affect and approach/withdrawal measures with resting frontal alpha asymmetry. The results revealed that the microstate-based resting frontal alpha asymmetry model correlated significantly with negative affect, and its neural underpinning’s HLI significantly correlated with positive/Negative affect and approach/withdrawal measures. Thus, the novel microstate-based microstate-based resting frontal alpha asymmetry model proved efficacious in bringing forth the association with affect and approach/withdrawal measures. ix In addition, to understand the role of subcortical regions, and their interaction with cortical regions in bringing forth the hemispheric asymmetries of affect and approach/withdrawal behavior, a study based on the hemispheric asymmetry model of resting fMRI graph theory functional connectivity metrics was carried out, as the viability to detect subcortical signals through EEG is still debatable. In this analysis, we report the neuroimaging finding based on Region of Interest (ROI) based analysis and graph-theory measures for global networks and subnetworks. The study revealed the involvement of emotion and memory-related subcorticalcortical interactions in positive and negative affect and basal ganglia structures in approachwithdrawal dichotomy. Further, lateralization of the strength of degree-measures of the corticalregions vital for subcortical-cortical interaction revealed higher connectivity within the lefthemisphere for affective measures. Thus, the current thesis demonstrates the benefit of assessing the standard resting hemispheric asymmetry model before a complex cognitive task such as SA, which holds paramount importance for the ergonomics community and for military/aviation domains. Further, the outcomes also offer an unprecedented attempt towards the development of a novel microstates-based resting hemispheric asymmetry model for bringing forth the relationship of resting EEG based asymmetry with psychological measures of affect and approach/withdrawal behavior Also, the key findings of subcortical regions and their interaction with cortical regions dominating the affect and approach/withdrawal measure can be further explored in clinical as well as task-based studies.

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Ranson, VA. "Auditory misattribution and schizotypy." Thesis, 2020. https://eprints.utas.edu.au/36044/1/Ranson_whole_thesis_ex_pub_mat.pdf.

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In schizophrenia, verbal auditory hallucinations (VAH) form one of the most distressing and hard-to-treat symptoms. The causes and neural basis of VAH are not well known and recruitment to schizophrenia research in this area is difficult. As an alternative, the personality construct of schizotypy in healthy individuals is a potential surrogate for schizophrenia. In one model of schizophrenia, schizotypy is considered a trait shared across the population, with high levels representing a risk factor for schizophrenia-related illness. Attenuated psychosis-like symptoms (including VAH) occur in some nonclinical individuals, without causing distress or impairment. Another theory proposes schizotypy as a liability to schizophrenia found in 10% of individuals, conferred by a heritable neural defect arising in embryo; schizophrenia may later develop under the influence of an environmental event. Within this context, my thesis marries two interests: (1) in the genesis of VAH through misattribution of internal auditory stimuli (a person’s own thought) due to deficits in sensory processing and attention; (2) in schizotypy as a schizophrenia surrogate in research, due to its theoretical affinity with schizophrenia. Specifically, I sought evidence from electroencephalography (EEG) studies to support the use of schizotypy to examine problems in auditory processing and attention in schizophrenia, and enquired whether nonclinical schizotypy is a genuine surrogate for schizophrenia. A meta-analysis of EEG microstates associated with schizophrenia showed that microstate deficiency may contribute to VAH. Meta-regression of the same data revealed that microstate deficits associated with schizophrenia did not vary with level of schizotypy. Then, an event-related potential study investigated an index of early auditory change detection that arises with or without conscious attention to a stimulus: the mismatch negativity (MMN). I tested whether MMN amplitude or latency varied with level of schizotypy in a nonclinical sample. Results weakly supported variation in some MMN metrics consistent with deficits in schizophrenia but not in others. Overall, nonclinical schizotypy is unlikely to represent a valid surrogate for schizophrenia in MMN research, at least on present evidence.

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Wei-Ming, Huang, and 黃韋銘. "Study on Microstate Transition of Alpha Rhythm and EEG Field Mass Center during Chan Ding and Rest." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/25823912754317217893.

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碩士
國立交通大學
電控工程研究所
103
This thesis is aimed to investigate the temporal evolution of spatial microstates of 30-channel Chan-Ding and resting EEG (electroencephalograph). Two different schemes of microstate analysis are developed in this study. The first scheme, CWT-NSAD, is based on spatial distribution of alpha power. The EEG signal is first decomposed into five EEG rhythms (∆, θ, α, β, and γ) by continuous Wavelet Transform (CWT). The percentage of  power is used to identify whether a 0.25-second epoch is α-dominant. For all the -dominant EEG epochs, the feature vectors composed of 30 normalized-to-unity (NU) alpha powers are classified by NSAD (normalized sum of absolute difference) based strategy. The strategy proposed in this thesis successfully classifies the EEG brain mappings of NU alpha powers. Performance of NSAD-based strategy is superior to FCM (fuzzy C-means) clustering. Finally, the classification results can be employed in long-term EEG interpretation and analysis of microstate behaviors. The second scheme, PMC (mass center of peak), directly evaluates the mass center of the brain electrical-potential field constructed for the major peaks of channel Fz. Channel Fz often picks up eye-motion artefacts that cause serious baseline drift. We apply linear regression to EEG baseline-drift correction. After the baseline correction, peak detection is employed to identify the major positive and negative peaks of substantial amplitude. The PMC microstate analysis is conducted on the major peaks. Each instantaneous microstate is quantified by the spatially geometric coordinates of centers of mass evaluated for major positive and negative peaks (positive and negative PMCs). Finally, we may explore the microstate behaviors from the spatial transition of positive and negative PMC in five regions (frontal, left temporal, right temporal, central, and posterior region). In our preliminary results, CWT-NSAD reveals the extraordinarily consistent -power level through the entire Chan-Ding EEG record, particularly in the frontal region. PMC discloses the rapid and frequent microstate transitions between frontal and central regions (68 FC or CF in one minute) in Chan-Ding EEG compared with the resting EEG (46 transitions in one minute). In addition, Chan-Ding positive PMC visits the posterior region (probability of 2.3%) less often than resting PMC (5.3%).

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Book chapters on the topic "EEG MICROSTATES"

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Lehmann, D. "Microstates of the Brain in EEG and ERP Mapping Studies." In Springer Series in Brain Dynamics, 72–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74557-7_6.

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Jia, Huibin. "Microstate Analysis." In EEG Signal Processing and Feature Extraction, 141–57. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2_8.

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Zhang, Li, Bo Shi, Mingna Cao, Sai Zhang, Yiming Dai, and Yanmei Zhu. "Identifying EEG Responses Modulated by Working Memory Loads from Weighted Phase Lag Index Based Functional Connectivity Microstates." In Communications in Computer and Information Science, 441–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36808-1_48.

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Zhang, Li, Mingna Cao, and Bo Shi. "Identifying Gifted Thinking Activities Through EEG Microstate Topology Analysis." In Neural Information Processing, 123–30. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46687-3_13.

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Koizumi, Koji, Fukashi Mikami, Kazutaka Ueda, and Masayuki Nakao. "EEG Microstate Characteristics in Product Conceptual Design: Increased Time Coverage of Microstate Class Related to the Default Mode Network." In Design Computing and Cognition’22, 215–33. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20418-0_14.

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Bonnstetter, Ronald, Thomas F. Collura, Carlos Zalaquett, and Huai-Hsuan Wang. "Electroencephalography microstates in relation to emotional decision-making." In Introduction to Quantitative EEG and Neurofeedback, 3–15. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-323-89827-0.00009-7.

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Hari, Riitta, and Aina Puce. "Analyzing the Data." In MEG - EEG Primer, edited by Riitta Hari and Aina Puce, 173—C10P325. 2nd ed. Oxford University PressNew York, 2023. http://dx.doi.org/10.1093/med/9780197542187.003.0010.

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Abstract This chapter discusses the strengths, pitfalls, and practicalities of MEG and EEG data analysis methods and visualization strategies. Data-set segmentation, signal-to-noise considerations, signal levels, and power are examined as these may drive the chosen data analysis strategy. After basic analyses of averaged and unaveraged data, brain microstates, event-related desynchronization/synchronization, temporal spectral evolution, and time-frequency analyses, phase synchronization, and cross-frequency coupling are discussed. Measures of the introduced association and functional/effective connectivity, as studied in the time or frequency domains, include correlation, coherence, phase-locking factor, phase-locking value, phase-lag index and their variants, mutual information, transfer entropy, cross-correlation, Granger causality, dynamic causal modeling, and graph-theoretical analysis. The MEG/EEG source modeling section covers forward and inverse problems, head models, single- and multidipole models, distributed models, and beamformers. After discussion of spatial resolution, source extent, and effects of synchrony complete the topics, the chapter ends with statistical considerations regarding signal detectability in individual and group-level data.

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Conference papers on the topic "EEG MICROSTATES"

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Zoubi, Obada Al, Ahmad Mayeli, Vadim Zotev, Hazem Refai, Martin Paulus, and Jerzy Bodurka. "POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS." In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2018. http://dx.doi.org/10.1109/globalsip.2018.8646521.

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Nguyen, Philon, Thanh An Nguyen, and Yong Zeng. "Measuring the Evoked Hardness of Design Problems Using Transient Microstates." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46502.

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Direct interfacing of computers with the human brain is one of the holy grails of computer science and has been in the computing folklore since the very beginning of computing history. The challenges researchers are facing are non-trivial and the breakthroughs are non-negligeable. Measuring the hardness of a mental task is a fundamental problem in design sciences. In this context, the relationship between electroencephalograms (EEG) signals and the design process is an area of research with applications to the understanding of the creative process and next generation CAD/E systems. Such systems are aiming at becoming more collaborative, conceptual, creative and cognitive. We posit that the relationship between EEG signals, cognitive states and the perceived hardness of design problems is non-trivial. Different problems typically have different levels of perceived hardness. To test this, we study the use of microstate analysis to the segmentation of videos of subjects submitted to creative tasks of various difficulty. Problems and subtasks of different perceived hardness can be shown to exhibit different levels of transient microstates, a measure we have defined on the complexity of the microstate segmentation. We show that the human brain seems to be using 1–20% of its transient microstate capacity.

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M. Alves, Lorraine, Klaus F. Côco, Mariane L. de Souza, and Patrick M. Ciarelli. "Graph Theory Analysis of Microstates in Attention-Deficit Hyperactivity Disorder." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1481.

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Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most common disorders of childhood and youth. The diagnosis of ADHD remains essentially clinical, based on history and questionnaires for symptom assessment, therefore, a biomarker can be of great value to reduce the inherent uncertainty of clinical diagnosis. In recent years, several studies have been carried out to assess the usefulness of neurophysiological (electroencephalography - EEG)and functional image data to assist in the process of diagnosing ADHD. Previous researches have revealed evidences that microstates are selectively affected by ADHD, indicating that their analysis may be a useful tool in methods of automatic disease identication. In this paper is proposed a new methodology for the detection of ADHD using EEG microstate analysis and graph theory. The proposed method allows modeling and interpreting each microstate as a complex network, which permits to identify the effect of ADHD on some characteristics of the built networks. In addition, it provides useful information to identify ADHD and subtypes patients with an accuracy around 99%, indicating that the proposed method is promising.

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Shaw, Laxmi, and Aurobinda Routray. "EEG Traced Microstates Detection during Meditation- A State of Consciousness." In 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2020. http://dx.doi.org/10.1109/iciis51140.2020.9342712.

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Vetró, Mihály, and Gábor Hullám. "Analyzing the Discriminative Power of EEG Microstates Over Mental Tasks." In 30th Minisymposium of the Department of Measurement and Information Systems. Online: Budapest University of Technology and Economics, 2023. http://dx.doi.org/10.3311/minisy2023-006.

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Minguillon, J., E. Pirondini, M. Coscia, R. Leeb, J. D. R. Millan, D. Van De Ville, and S. Micera. "Modular organization of reaching and grasping movements investigated using EEG microstates." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6944029.

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Piorecky, Marek, and Stepanka Padevetova. "Microstates as a Tool for Identifying Dreaming in a Sleep EEG." In 2021 International Conference on e-Health and Bioengineering (EHB). IEEE, 2021. http://dx.doi.org/10.1109/ehb52898.2021.9657703.

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Luo, Na, Xiangsheng Luo, Dongren Yao, Vince D. Calhoun, Li Sun, and Jing Sui. "Investigating ADHD subtypes in children using temporal dynamics of the electroencephalogram (EEG) microstates." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9630614.

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Shen, Xinke, Xin Hu, Shizhao Liu, Sen Song, and Dan Zhang. "Exploring EEG microstates for affective computing: decoding valence and arousal experiences during video watching*." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9175482.

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Qiu, Shuang, Shengpei Wang, Weibo Yi, Chuncheng Zhang, and Huiguang He. "Changes of resting-state EEG microstates induced by low-frequency repetitive transcranial magnetic stimulation." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9176673.

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