Relevant bibliographies by topics / Microstate analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Microstate analysis'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Microstate analysis"

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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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Dissertations / Theses on the topic "Microstate analysis"

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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 an
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Li, Chang-Yi, and 李昶毅. "Microstate Analysis of Zen-Meditation Brain Topography." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/80182219105324076123.

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碩士<br>國立交通大學<br>電機與控制工程系所<br>96<br>The aim of this study is to propose a method for detecting alpha wave in EEG (electroencephalograph) and analyzing the alpha spatial characteristics in a microstate aspect. We investigated and compared the brain microstates between Zen-meditation practitioners (experimental group) and non-practitioners (control group). Firstly, EEG epochs of interest were extracted by alpha-power percentage that is at least fifty percent of total power. In the analysis, wavelet decomposition and reconstruction was adopted. Then Mahalanobis Fuzzy C-means clustering was employe
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Huang, Bo-Jun, and 黃柏鈞. "Dynamic Analysis for a Piezo-driven Microstage Structure." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/66425857317861858993.

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碩士<br>中原大學<br>光機電及資電控產業研發碩士專班<br>98<br>Abstract The purpose of this study is to discuss the structural rigidity and dynamic analysis of high-frequency nanoscale cutting system. Nanoscale cutting system contains three substructures, includes fixed tools base, micro positioning stage and ball screw stage. ANSYS software with the finite element method is used to analyze each substructure separately and complete nanoscale cutting structure. Resonant frequency, mode shape, dynamic stiffness and dynamic analysis are investigated and discussed. Modal analysis is carried out by using Block Lanczos Me
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Hsu, Chao-chen, and 徐肇辰. "A Study on the Mechanism Design and Analysis of Microstages for Microassembly." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/70805566890100641823.

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碩士<br>國立中山大學<br>機械與機電工程學系研究所<br>92<br>Accompanying with the development of MEMS technology, microstages have been used for many years. Most of the studies on microstages have been aimed at the application of new actuators, materials and fabrication process in recent years. However, the systematic way for designing new microstages with the mechanism conceptual design approach still needs some more input. 　　The objective of this study is to establish a methodology to design new microstages employing the concept of mechanism design. First of all, new microstages for microassembly have been a
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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 simp
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Book chapters on the topic "Microstate analysis"

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

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

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Thukral, Sanat, Bujar Raufi, and Bojan Božic. "Convolutional Autoencoder-Based Dimensionality Reduction for EEG Microstate Analysis." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-96235-6_6.

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Razo-Guevara, Daniela Fernanda, Israel Román-Godínez, Ricardo A. Salido-Ruiz, Héctor H. Alfaro-Cortés, Stewart R. Santos Arce, and Sulema Torres-Ramos. "Assessing Changes in Brain Activity Through Microstate Analysis in Serious Video Game Training." In IFMBE Proceedings. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82123-3_24.

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Jia, Wenjun, Hongjiang Yu, Morteza Zangeneh Soroush, and Yong Zeng. "Higher-Order Cognitive States Recognition in Open-Ended Design Creation Tasks Using EEG Microstate Analysis and Convolutional Neural Networks." In Design Computing and Cognition’24. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71922-6_11.

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Oppermann, Hannes, Patrique Fiedler, and Jens Haueisen. "Microstates Analysis for Dry and Gel-Based Multichannel Electroencephalography." In IFMBE Proceedings. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61628-0_14.

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Fu, Yi, Xin Hua Ji, and Yu Wen Qin. "Experimental Study of Micro Displacement Field of Microstate of Crack Tips of Ceramics Plasticized with Zirconia and Stabilized by Yttrium Oxide – Application of Digital Image Correlation Method Based on Analysis by Scanning Electron Microscope." In Key Engineering Materials. Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/0-87849-410-3.2436.

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Maimone, João Hugo Marinho, Thiago Franco de Carvalho Dias, Fellipe Guilherme Rey de Souza, and Rodrigo Martins Pagliares. "MicroSTAMP: Microservices for Steps 1 and 2 of the System-Theoretic Process Analysis (STPA) Technique." In Advances in Intelligent Systems and Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56599-1_59.

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Strik, Werner K., and Thomas Koenig. "Spatial analysis of ERP and EEG data." In Schizophrenia: From neuroimaging to neuroscience. Oxford University PressOxford, 2004. http://dx.doi.org/10.1093/oso/9780198525967.003.0012.

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Abstract The extended scalp potential differences that are measured by electroencephalography (EEG), magneto encephalography (MEG), and event related potentials (ERPs) result directly from the coherent firing of large, predominantly cortical, neural populations. Theoretically, EEG and ERPs, therefore, offer views on brain functions that no other non-invasive technique can provide; these include different modes of oscillation (frequency analysis), transient functional coupling of brain regions (event related synchronization and desynchronization), or temporal sequencing of brain events in the s
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Hari, Riitta, and Aina Puce. "Analyzing the Data." In MEG - EEG Primer, 2nd ed., edited by Riitta Hari and Aina Puce. 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
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Conference papers on the topic "Microstate analysis"

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Wang, Yuxin, Zhen Zhang, Jiang Wang, Xiaodong Zhu, Xuze Bai, and Chen Liu. "Temporal variability analysis of frequency-optimized Parkinsonian mild cognitive impairment’s microstate network." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662614.

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Zeng, Yining, Youchao Sun, Jia Yang, and Zhe Zeng. "Brain Activity Patterns of Pilot Cognitive Process: An EEG Microstate Analysis Study." In 2024 10th International Conference on Systems and Informatics (ICSAI). IEEE, 2024. https://doi.org/10.1109/icsai65059.2024.10893731.

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Lu, Jingqing, Hongli Chang, Xiaoyan Tian, et al. "Dynamic Analysis in Resting-State EEG Based on Natural Stimuli via Power Spectra and Microstate." In 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2024. https://doi.org/10.1109/cisp-bmei64163.2024.10906236.

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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 selective
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Cayeux, Eric, John Macpherson, Dimitrios Pirovolou, Moray Laing, and Fred Florence. "A General Framework to Describe Drilling Process States." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212537-ms.

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Abstract Automation and digitalization of drilling requires shared knowledge about the state of the drilling process: is the bit on-bottom drilling or is the driller making a connection; is the borehole in good condition or is it sloughing? Yet there is no shared, clear and usable definition of what a drilling process state is, nor an agreed method to calculate it. In this paper, we propose a method to clarify the concept of drilling process state. A set of partial differential equations, respecting boundary conditions, can describe drilling operations. The set of all possible discrete changes
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Salagean, Andreea, Andreea-Madalina Pasc, Eugen Richard Ardelean, et al. "Local Field Potential Microstate Analysis." In 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2022. http://dx.doi.org/10.1109/iccp56966.2022.10053960.

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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 system
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Dipietro, L., M. Plank, H. Poizner, and H. I. Krebs. "EEG microstate analysis in human motor corrections." In 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2012). IEEE, 2012. http://dx.doi.org/10.1109/biorob.2012.6290832.

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Chen, Liu, Liu Shang, Chu Chunguang, Wang Jiang, and Cai Lihui. "Spatiotemporal electroencephalography microstate analysis in disorders of consciousness." In 2022 41st Chinese Control Conference (CCC). IEEE, 2022. http://dx.doi.org/10.23919/ccc55666.2022.9901837.

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Michalopoulos, Kostas, and Nikolaos Bourbakis. "Microstate analysis of the EEG using local global graphs." In 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2013. http://dx.doi.org/10.1109/bibe.2013.6701583.

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Reports on the topic "Microstate analysis"

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Wei, Ran, Yonglu Wang, Hui Fang, et al. A Meta-Analysis on Electrophysiological Microstates in Autism Spectrum Disorder. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.10.0034.

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Rathbun, W. From chaos to order: The MicroStar data acquisition and analysis system. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5147502.

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