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: 11 February 2022

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Journal articles on the topic "Microstate analysis"

1

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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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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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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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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Kim, Kyungwon, Nguyen Thanh Duc, Min Choi, and Boreom Lee. "EEG microstate features for schizophrenia classification." PLOS ONE 16, no. 5 (May 14, 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, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.
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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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Ruiz, Pablo, Raquel Tinoco-Egas, and Carlos Cevallos. "Neural States in Tourism Travel Videos." Proceedings 71, no. 1 (November 18, 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 Hierarchical Clustering (TAAHC) microstate analysis for the duration of the EEG as the participants watched each video. We picked the four predominant, in total time and repeatability, microstate maps that represent more than 50% of the entire recording time. We also show, in time, how topographies are represented along the video, which in a later step could be correlated with the images observed in the videos. We show the existing relations between the existing microstates. A microstate analysis of brain signal behavior across time might be a valid methodology and useful tool to analyze videos with marketing purposes.
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Liang, Andi, Shanguang Zhao, Jing Song, Yan Zhang, Yue Zhang, Xiaodan Niu, Tao Xiao, and Aiping Chi. "Treatment Effect of Exercise Intervention for Female College Students with Depression: Analysis of Electroencephalogram Microstates and Power Spectrum." Sustainability 13, no. 12 (June 16, 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 during the intervention. Compared with those in the depression group, the power spectrum values in the θ and α bands were significantly decreased (p < 0.05), and the duration of microstate C increased significantly (p < 0.05), while the frequency of microstate B decreased significantly (p < 0.05) in the exercise intervention group. The transition probabilities showed that the exercise intervention group had a higher probability from B to D than those in the depression group (p < 0.01). In addition, the power of the δ and α bands were negatively correlated with the occurrence of microstate C (r = −0.842, p < 0.05 and r = −0.885, p < 0.01, respectively), and the power of the β band was positively correlated with the duration of microstate C (r = 0.900, p < 0.01) after exercise intervention. Our results suggest that the decreased duration of microstate C and the increased α power in depressed students are associated with reduced cognitive ability, emotional stability, and brain activity. Depression symptoms were notably improved after exercise intervention, thus providing a more scientific index for the research, rehabilitation mechanisms, and treatment of depression.
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Milz, P. "Keypy – An Open Source Library For EEG Microstate Analysis." European Psychiatry 33, S1 (March 2016): S493. http://dx.doi.org/10.1016/j.eurpsy.2016.01.1812.

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The brain's electric field configuration reflects its momentary, global functional state. The fluctuations of these states can be analyzed at millisecond resolution by the EEG microstate analysis. This analysis reportedly allowed the detection of brain state duration, occurrence, and sequence aberrations in psychiatric disorders such as schizophrenia, dementia, and depression. Several existing software solutions implement the microstate analysis, but they all require extensive user-interaction. This represents a major obstacle to time-efficient automated analyses and parameter exploration of large EEG datasets. Scriptable programming languages such as Python provide a means to efficiently automate such analysis workflows.For this reason, I developed the KEY EEG Python Library keypy. This library implements all steps necessary to compute the microstate analysis based on artefact free segments of EEG. It includes functions to carry out the necessary preprocessing (data loading, filtering, average referencing), modified k-means clustering based microstate identification, principal component based mean computation (across recording runs, conditions, participants, and or participant groups), and to retrieve the microstate class based statistics necessary to compare microstate parameters between groups and/or conditions. Keypy is an open source library and freely available from https://www.github.com/keyinst/keypy.Keypy provides a platform for automated microstate analysis of large-scale EEG datasets from psychiatric patient populations and their comparison to healthy controls. It is easily applicable and allows efficient identification of deviant brain states in clinical conditions.Disclosure of interestThe authors have not supplied their declaration of competing interest.
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Hu, Yanzhu, Zhen Meng, Xinbo Ai, Han Li, Yu Hu, and Huiyang Zhao. "Hybrid Feature Extraction of Pipeline Microstates Based on Φ-OTDR Sensing System." Journal of Control Science and Engineering 2019 (September 22, 2019): 1–10. http://dx.doi.org/10.1155/2019/6087582.

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This paper proposes a general integration method which can effectively describe the characteristics of pipeline leakage and help distinguish multiple pipeline microstates. Since the rapid development of Φ-OTDR in recent years, this technology has been applied to more and more fields, such as fiber optic safety monitoring, seismic monitoring, and structural health monitoring. Among them, Φ-OTDR has the characteristic of continuous full-scale monitoring in pipeline monitoring, but there are few researches on pipeline state characteristics at present. In this paper, based on the analysis of the pipeline state with Φ-OTDR technology, a method of extracting multiple microstates of pipelines is proposed. This method combined with the peak-to-average power ratio, short-term interval zero crossing, and fractal characteristics in the frequency domain can effectively characterize the microstate of pipes and provide support for identification of more microstates of pipelines. These features reflect the common characteristics of leaks in gas pipelines and liquid pipelines. Meanwhile, their combination features can represent the small differences in pipeline states. The experimental results show that the method can effectively characterize the microstate information of the pipeline, and the recognition rate of the hybrid feature under two kinds of pipeline leakage and multipressure conditions reaches 91% and 83%.
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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 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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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
國立交通大學
電機與控制工程系所
96
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 employed in the classification scheme for various alpha mappings. Finally, the alpha-brain microstates were explored and compared for both experimental and control groups. The preliminary results reveal a longer duration of frontal-alpha microstate observed in Zen-meditation practitioners in comparison with control subjects. From the literatures, a longer duration of microstate may imply that the brain is involved in slight information processing, reflecting a rather stabilized dynamics.
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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
中原大學
光機電及資電控產業研發碩士專班
98
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 Method to create the model. The rate of decay of oscillation of the material is measured from the experiment. Appropriate damping coefficient is found by comparing the simulation and experimental result through transient analysis. Harmonic analysis of Mode Superposition is used to verify the displacement direction of the micro positioning stage with the chosen mode shape that is suitable to piezoelectric actuator, and the structure stiffness of the entire system is satisfactory.
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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
國立中山大學
機械與機電工程學系研究所
92
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 analyzed according to the basic requirements from the mechanism. Afterwards, the concept of microjoint has been presented and used in the synthesis of microstages. Besides, a flow chart of design procedure has been presented and seven kinds of microstages are achieved accordingly. Finally, the FEM simulation of the synthesized microstage illustrates the desirable results that reveal the good agreement with the expected motion. It is shown that this study can be efficiency applied to the design of micro scale devices.
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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, 141–57. Singapore: 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, 123–30. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46687-3_13.

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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, 2436–39. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/0-87849-410-3.2436.

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"Face-Elicited ERPs and Affective Attitude: Brain Electric Microstate and Tomography Analyses." In Foundations in Social Neuroscience. The MIT Press, 2002. http://dx.doi.org/10.7551/mitpress/3077.003.0042.

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Conference papers on the topic "Microstate analysis"

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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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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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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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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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Pei-Chen Lo and Qiang Zhu. "Microstate analysis of alpha-event brain topography during chan meditation." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212377.

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Si, Lichengxi, Zhian Liu, and Gang Wang. "Depth of Anesthesia Monitoring Method Based on EEG Microstate Analysis and Hidden Markov Model." In 2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML). IEEE, 2021. http://dx.doi.org/10.1109/prml52754.2021.9520709.

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Thuraisingham, R. A., Y. Tran, A. Craiga, N. Wijesuriya, and Hung Nguyen. "Using microstate intensity for the analysis of spontaneous EEG: Tracking changes from alert to the fatigue state." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5334094.

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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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Walraven, Jeremy A., and Bernhard Jokiel, Jr. "Failure analysis of a multi-degree-of-freedom spatial microstage." In Micromachining and Microfabrication, edited by Rajeshuni Ramesham and Danelle M. Tanner. SPIE, 2003. http://dx.doi.org/10.1117/12.478207.

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Kelsey, Matthew, Fred W. Prior, and Linda J. Larson-Prior. "Spatiotemporal analysis of the appearance of gamma-band Microstates in resting state MEG." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7318933.

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Reports on the topic "Microstate analysis"

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

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