Analytical Methods of EEG Microstates and Applications in Cognitive Impairment Patient
Li Zipeng1,2, Li Xin1,2, Qu Zhongjie1,2, Su Rui3, Yin Bowen4, Yin Liyong4*
1(School of Electrical Engineering,Yanshan University, Qinhuangdao 066004,Hebei, China) 2(Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao 066004, Hebei, China) 3(School of Medical Imaging, Hebei Medical University, Shijiazhuang 050011, China) 4(Department of Neurology, The First Hospital of Qinhuangdao, Qinhuangdao 066000, Hebei, China)
Abstract：Electroencephalogram (EEG) microstates can characterize the spatiotemporal dynamic network reorganization in the brain by capturing transient stable patterns of global potential distribution. The core value lies in resolving the discontinuous transitions and non-stationary conversion characteristics of functional brain states. However, conventional analysis methods for the microstates face challenges in standardization due to technical heterogeneity in workflows and classification uncertainty, limiting the comparability of cross-study results. This review systematically elaborated the fundamental principles and extraction procedures of microstate analysis, covering key technical steps including signal preprocessing, frequency band optimization, and clustering method selection. Rresearch advancements in traditional feature parameters and dynamic syntactic characteristics were summarized. Focusing on cognitive impairment research, this article discussed current applications of the microstate analysis in patients with Alzheimer′s disease (AD) and mild cognitive impairment (MCI), including feature characterization, disease diagnosis, and cognitive assessment. Finally, potential challenges and future research directions were discussed, with the aim of providing valuable references for researchers in this field.
[1] Burle B, Spieser L, Roger C, et al. Spatial and temporal resolutions of EEG: is it really black and white? a scalp current density view[J]. International Journal of Psychophysiology, 2015,97(3):210-220. [2] Lehmann D, Ozaki H, Pal I. EEG alpha map series: brain micro-states by space-oriented adaptive segmentation[J]. Electroencephalogr Clin Neurophysiol, 1987,67(3):271-288. [3] Croce P, Quercia A, Costa S, et al. EEG microstates associated with intra- and inter-subject alpha variability[J]. Scientific Reports, 2020,10(1):2469. [4] Lehmann D, Strik WK, Henggeler B, et al. Brain electric microstates and momentary conscious mind states as building blocks of spontaneous thinking: I. visual imagery and abstract thoughts[J]. International Journal of Psychophysiology, 1998,29(1):1-11. [5] Khanna A, Pascual-Leone A, Michel CM, et al. Microstates in resting-state EEG: current status and future directions [J]. Neuroscience and Biobehavioral Reviews, 2015,49:105-113. [6] Scheltens P, De Strooper B, Kivipelto M, et al. Alzheimer′s disease[J]. The Lancet, 2021, 397:1577-1590. [7] Xiao J, Li J, Wang J, et al. China Alzheimer′s disease: facts and figures[J]. Human Brain, 2023,2(3):1-13. [8] Pascual-Marqui RD, Michel CM, Lehmann D. Segmentation of brain electrical activity into microstates: model estimation and validation[J]. IEEE Transactions on Biomedical Engineering, 1995,42(7):658-665. [9] Milz P, Pascual-Marqui RD, Achermann P, et al. The EEG microstate topography is predominantly determined by intracortical sources in the alpha band[J]. Neuroimage, 2017,162:353-361. [10] Férat V, Seeber M, Michel CM, et al. Beyond broadband: towards a spectral decomposition of electroencephalography microstates [J]. Human Brain Mapping, 2022,43(10):3047-3061. [11] Javed E, Croce P, Zappasodi F, et al. Normal aging: alterations in scalp EEG using broadband and band-resolved topographic maps [J]. Frontiers in Physics, 2020,8:82. [12] Terpou BA, Shaw SB, Théberge J, et al. Spectral decomposition of EEG microstates in post-traumatic stress disorder[J]. Neuroimage-Clinical, 2022,35:103135. [13] Haydock D, Kadir S, Leech R, et al. EEG microstate syntax analysis: a review of methodological challenges and advances[J]. Neuroimage, 2025,309:121090. [14] Lloyd S. Least squares quantization in PCM. [J] IEEE Transactions on Information Theory, 1982, 28 (2),129-137. [15] Khanna A, Pascual-Leone A, Farzan F, et al. Reliability of resting-state microstate features in electroencephalography [J]. PLoS ONE, 2014, 9 (12): e114163. [16] Murray MM, Brunet D, Michel CM. Topographic ERP analyses: a step-by-step tutorial review[J]. Brain Topography, 2008, 20(4): 249-264. [17] Toffoli L, Zdorovtsova N, Epihova G, et al. Dynamic transient brain states in preschoolers mirror parental report of behavior and emotion regulation [J]. Human Brain Mapping, 2024, 45 (14): e70011. [18] Jajcay N, Hlinka J. Towards a dynamical understanding of microstate analysis of M/EEG data[J]. Neuroimage, 2023,281:120371. [19] Custo A, Van De Ville D, Wells WM, et al. Electroencephalographic resting-state networks: source localization of microstates[J]. Brain Connectivity, 2017,7(10):671-682. [20] Britz J, Van De Ville D, Michel CM, et al. BOLD correlates of EEG topography reveal rapid resting-state network dynamics[J] NeuroImage, 2010,52(4):1162-1170. [21] Al Zoubi O, Mayeli A, Misaki M, et al. Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal[J]. Journal of Neural Engineering, 2021,18(6): 066051. [22] Korn U, Krylova M, Heck KL, et al. EEG-microstates reflect auditory distraction after attentive audiovisual perception recruitment of cognitive control networks [J]. Frontiers in Systems Neuroscience, 2022, 15:751226. [23] Zhao ZY, Ran XY, Lv SY, et al. Causal link between prefrontal cortex and EEG microstates: evidence from patients with prefrontal lesion[J]. Frontiers in Neuroscience, 2024,17:1306120. [24] Musaeus CS, Engedal K, Hogh P, et al. Changes in the left temporal microstate are a sign of cognitive decline in patients with Alzheimer′s disease[J]. Brain and Behavior, 2020, 10(6): e01630. [25] Pipinis E, Melynyte S, Koenig T, et al. Association between resting-state microstates and ratings on the Amsterdam Resting-State Questionnaire[J]. Brain Topography, 2017, 30(2): 245-248. [26] Tarailis P, Simkute D, Koenig T, et al. Relationship between spatiotemporal dynamics of the brain at rest and self-reported spontaneous thoughts: an EEG microstate approach[J]. Journal of Personalized Medicine, 2021,11(11): 1216. [27] Si XP, Han SL, Zhang K, et al. The temporal dynamics of EEG microstate reveals the neuromodulation effect of acupuncture with DEQI[J]. Frontiers in Neuroscience, 2021, 15:715512. [28] Bréchet L, Brunet D, Birot G, et al. Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI[J]. Neuroimage, 2019,194: 82-92. [29] Teng CL, Cong L, Tian QM, et al. EEG microstate in people with different degrees of fear of heights during virtual high-altitude exposure[J]. Brain Research Bulletin, 2024,218:111112. [30] Ke M, Li JP, Wang LB. Alteration in resting-state EEG microstates following 24 hours of total sleep deprivation in healthy young male subjects[J]. Frontiers in Human Neuroscience, 2021,15:636252. [31] Stoffers D, Diaz BA, Chen G, et al. Resting-state fMRI functional connectivity is associated with sleepiness, imagery, and discontinuity of mind[J]. PLoS ONE, 2015,10(11): e0142014. [32] Wei ZH, Wang XP, Liu C, et al. Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: a machine learning approach[J]. Neuroimage,2024,296:120683. [33] Von Wegner F, Wiemers M, Hermann G, et al. Complexity measures for EEG microstate sequences: concepts and algorithms[J]. Brain Topography, 2024,37(2):298-311. [34] Ren HC, Ran XY, Qiu MY, et al. Abnormal nonlinear features of EEG microstate sequence inobsessive-compulsive disorder[J].BMC Psychiatry, 2024,24(1):881. [35] Zhao ZY, Niu YX, Zhao XF, et al. EEG microstate in first-episode drug-naive adolescents with depression[J]. Journal of Neural Engineering, 2022,19(5):056016. [36] Tait L, Tamagnini F, Stothart G, et al. EEG microstate complexity for aiding early diagnosis of Alzheimer′s disease [J]. Scientific Reports, 2020, 10(1):17627. [37] NehanivCL,Antonova E. Simulating and reconstructing neurodynamics with epsilon-automata applied to electroencephalography (EEG) microstate sequences [C]//IEEE Symposium Series on Computational Intelligence. Honolulu:IEEE, 2017:1753-1761. [38] Von Wegner F, Tagliazucchi E, Laufs H. Information-theoretical analysis of resting state EEG microstate sequences-non-markovianity, non-stationarity and periodicities[J]. Neuroimage, 2017, 158: 99-111. [39] Artoni F, Maillard J, Britz J, et al. Microsynt: exploring the syntax of EEG microstates[J]. Neuroimage, 2017, 277: 120196. [40] K Guan, Z Zhang, X Chai, et al. EEG based dynamic functional connectivity analysis in mental workload tasks with different types of information[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 632-642. [41] Chu C, Zhang Z, Wang J, et al. Temporal and spatial variability of dynamic microstate brain network in early Parkinson′s disease[J]. NPJ Parkinson′s Disease, 2023, 9(1):57. [42] Li ZP, Qu ZJ, Yin BW, et al. Functional connectivity key feature analysis of cognitive impairment patients based on microstate brain network[J]. Cerebral Cortex, 2024,34(2):1-10. [43] Yan TY, Wang GS, Liu TT, et al. Effects of microstate dynamic brain network disruption in different stages of schizophrenia[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, 31: 2688-2697. [44] Lian HP, Li YJ, Li YX, et al. Altered EEG microstate dynamics in mild cognitive impairment and Alzheimer′s disease[J]. Clinical Neurophysiology, 2021,132(11):2861-2869. [45] Wan W, Gu ZZ, Peng CK, et al. Beyond frequency bands: complementary-ensemble-empirical-mode-decomposition-enhanced microstate sequence non-randomness analysis for aiding diagnosis and cognitive prediction of dementia[J]. Brain Sciences, 2024, 14(5):487. [46] 李伟海, 陶华英.不同认知功能障碍静息态脑电微状态特征比较[J].中华老年心脑血管病杂志,2024,26(4):436-439. [47] Lin N, Gao J, Mao CH, et al. Differences in multimodal electroencephalogram and clinical correlations between early-onset Alzheimer′s disease and frontotemporal dementia [J]. Frontiers in Neuroscience, 2023, 15:687053. [48] Nobukawa S, Ikeda T, Kikuchi M, et al. Atypical instantaneous spatio-temporal patterns of neural dynamics in Alzheimer′s disease[J]. Scientific Reports,2024,14(1):88. [49] Teipel SJ, Brüggen K, Temp AGM, et al. Simultaneous assessment of electroencephalography microstates and resting state intrinsic networks in Alzheimer′s disease and healthy aging [J]. Frontiers in Neurology, 2021, 12:637542. [50] Supekar K, Menon V, Rubin D, et al. Network analysis of intrinsic functional brain connectivity in Alzheimer′s disease [J]. PLoS Computational Biology, 2008, 4(6): e1000100. [51] Celebi O, Uzdogan A, Oguz KK, et al. Default mode network connectivity is linked to cognitive functioning and CSF Aβ1-42 levels in Alzheimer′s disease[J]. Archives of Gerontology and Geriatrics, 2016, 62:125-132. [52] Smailovic U, Koenig T, Laukka EJ, et al. EEG time signature in Alzheimer′s disease: functional brain networks falling apart [J]. Neuroimage-Clinical, 2019,24:102046. [53] Tarailis P, Koenig T, Michel CM, et al. The functional aspects of resting EEG microstates: a systematic review[J], Brain Topography, 2024, 37(2):181-217. [54] Musaeus CS, Nielsen MS, Hogh P. Microstates as disease and progression markers in patients with mild cognitive impairment [J]. Frontiers in Neuroscience, 2019, 13:563. [55] Tarailis P, Lory K, Unschuld PG, et al. Self-related thought alterations associated with intrinsic brain dysfunction in mild cognitive impairment [J]. Scientific Reports, 2025, 15(1):12279. [56] Lassi M, Fabbiani C, Mazzeo S, et al. Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: early biomarkers along the Alzheimer′s Disease continuum? [J]. Neuroimage-Clinical, 2023, 38:103407. [57] 刘泽达,崔冬,李小俚,等.基于微状态的轻度认知障碍脑电信号特征研究[J].燕山大学学报,2023,47(4):370-376. [58] Yan YB, Gao MM, Geng Z, et al. Abnormal EEG microstates in Alzheimer′s disease: predictors of β-amyloid deposition degree and disease classification[J]. Geroscience, 2024, 46:4779-4792. [59] Shi YP, Ma QY, Feng CY, et al. Microstate feature fusion for distinguishing AD from MCI [J]. Health Information Science and Systems, 2022, 10(1):16. [60] Yang XL, Fan ZP, Li, ZW, et al. Resting-state EEG microstate features for Alzheimer′s disease classification[J]. PLoS ONE, 2024, 19(12): e0311958. [61] Hasan MM. Towards diagnosis of dementia: microstate analysis of EEG signals and classification using machine learning[D]. Oslo City: Oslo Metropolitan University, 2024. [62] Yao Q, Tang FY, Wang YY, et al. Effect of cerebellum stimulation on cognitive recovery in patients with Alzheimer disease: a randomized clinical trial[J]. Brain Stimulation,2022,15(4):910-920. [63] Satorres E, Torrella JE, Real E, et al. Home-based transcranial direct current stimulation in mild neurocognitive disorder due to possible Alzheimer′s disease[J]. Frontiers in Psychology, 2023, 13: 1071737. [64] Zhang YQ, Zhang ZY, Luo L, et al. 40 Hz light flicker alters human brain electroencephalography microstates and complexity implicated in brain diseases[J]. Frontiers in Neuroscience, 2021, 15: 777183. [65] Hanoglu L, Toplutas E, Saricaoglu M, et al. Therapeutic role of repetitive transcranial magnetic stimulation in Alzheimer′s and Parkinson′s disease: electroencephalography microstate correlates [J]. Frontiers in Neuroscience, 2022, 16:798558. [66] Amir G, Felix B, Michal K, et al. EEG microstates as a “real time” feedback for rTMS treatment efficacy in MCI subjects [J]. Alzheimers & Dementia, 2023, 19(s14): e075650. [67] Liu S, Yang S, Feng KK, et al. A study on the effects of repetitive transcranial magnetic stimulation on EEG microstate in patients with Parkinson′s disease[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024, 32:3369-3377. [68] Zhang HM, Yang X, Yao LQ, et al. EEG microstates analysis after TMS in patients with subacute stroke during the resting state[J]. Cerebral Cortex,2024,34(1): bhad480. [69] Che QY, Xi CH, Sun YL, et al. EEG microstate as a biomarker of personalized transcranial magnetic stimulation treatment on anhedonia in depression[J]. Behavioural Brain Research, 2025, 483: 115463. [70] Ding ZH, Wang Y, Niu ZK, et al. The effect of EEG microstate on the characteristics of TMS-EEG [J]. Computers in Biology and Medicine, 2024, 173: 108332. [71] Lucarelli D, Guidali G, Sulcova D, et al. Stimulation parameters recruit distinct cortico-cortical pathways: insights from microstate analysis on TMS-evoked potentials[J]. Brain Topography, 2025, 38(3): 39. [72] Wang YX, Li Q, Yao L, et al. Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer′s disease[J]. Cerebral Cortex, 2024, 34(3): bhae094. [73] Chen X, Li YJ, Li RR, et al. Multiple cross-frequency coupling analysis of resting-state EEG in patients with mild cognitive impairment and Alzheimer′s disease[J]. Frontiers in Aging Neuroscience, 2023, 15: 1142085. [74] Babiloni C, Carducci F, Lizio R, et al. Resting state cortical electroencephalographic rhythms are related to gray matter volume in subjects with mild cognitive impairment and Alzheimer′s disease[J]. Human Brain Mapping, 2013, 34(6):1427-1446.
