Dynamic analysis on simultaneous iEEG-MEG data via hidden Markov model
•We applied Hidden Markov Model (HMM) to concurrent MEG and intracranial EEG (iEEG).•Epilepsy cohort HMM analysis yielded states similar to those of healthy subjects.•iEEG power correlated with the time course of HMM states.•Functional clusters of iEEG electrodes agreed with those based on spatial l...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 233; p. 117923 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.06.2021
Elsevier Limited Academic Press Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-8119 1095-9572 1095-9572 |
| DOI | 10.1016/j.neuroimage.2021.117923 |
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| Abstract | •We applied Hidden Markov Model (HMM) to concurrent MEG and intracranial EEG (iEEG).•Epilepsy cohort HMM analysis yielded states similar to those of healthy subjects.•iEEG power correlated with the time course of HMM states.•Functional clusters of iEEG electrodes agreed with those based on spatial location.•Our pipeline can be used for group analysis of concurrent MEG and invasive data.
Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iEEG power changes that would allow classifying iEEG contacts into functional clusters.
Simultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy whose intracranial electrodes were implanted for pre-surgical evaluation. Pre-processed MEG sensor data was projected to source space. Time delay embedded HMM was then applied to MEG time series. At the same time, iEEG time series were analyzed with time-frequency decomposition to obtain spectral power changes with time. To relate MEG and iEEG results, correlations were computed between HMM probability time courses of state activation and iEEG power time course from the mid contact pair for each electrode in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Association of iEEG electrodes with HMM states based on significant correlations was compared to that based on the distance to peaks in subject-specific state topographies.
Five HMM states were inferred from MEG. Two of them corresponded to the left and the right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. All the electrodes had significant correlations with at least one of the states (p < 0.05 uncorrected) and for 27/50 electrodes these survived within-subject FDR correction (q < 0.05). These correlations peaked in the theta/alpha band. There was a highly significant dependence between the association of states and electrodes based on functional correlations and that based on spatial proximity (p = 5.6e−6,χ2 test for independence). Despite the potentially atypical functional anatomy and physiological abnormalities related to epilepsy, HMM model estimated from the patient group was very similar to that estimated from healthy subjects.
Epilepsy does not preclude HMM analysis of interictal data. The resulting group functional states are highly similar to those reported for healthy controls. Power changes recorded with iEEG correlate with HMM state time courses in the alpha-theta band and the presence of this correlation can be related to the spatial location of electrode contacts close to the individual peaks of the corresponding state topographies. Thus, the hypothesized relation between iEEG contacts and HMM states exists and HMM could be further explored as a method for identifying comparable iEEG channels across subjects for the purposes of group analysis. |
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| AbstractList | Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iEEG power changes that would allow classifying iEEG contacts into functional clusters.
Simultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy whose intracranial electrodes were implanted for pre-surgical evaluation. Pre-processed MEG sensor data was projected to source space. Time delay embedded HMM was then applied to MEG time series. At the same time, iEEG time series were analyzed with time-frequency decomposition to obtain spectral power changes with time. To relate MEG and iEEG results, correlations were computed between HMM probability time courses of state activation and iEEG power time course from the mid contact pair for each electrode in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Association of iEEG electrodes with HMM states based on significant correlations was compared to that based on the distance to peaks in subject-specific state topographies.
Five HMM states were inferred from MEG. Two of them corresponded to the left and the right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. All the electrodes had significant correlations with at least one of the states (p < 0.05 uncorrected) and for 27/50 electrodes these survived within-subject FDR correction (q < 0.05). These correlations peaked in the theta/alpha band. There was a highly significant dependence between the association of states and electrodes based on functional correlations and that based on spatial proximity (p = 5.6e
χ
test for independence). Despite the potentially atypical functional anatomy and physiological abnormalities related to epilepsy, HMM model estimated from the patient group was very similar to that estimated from healthy subjects.
Epilepsy does not preclude HMM analysis of interictal data. The resulting group functional states are highly similar to those reported for healthy controls. Power changes recorded with iEEG correlate with HMM state time courses in the alpha-theta band and the presence of this correlation can be related to the spatial location of electrode contacts close to the individual peaks of the corresponding state topographies. Thus, the hypothesized relation between iEEG contacts and HMM states exists and HMM could be further explored as a method for identifying comparable iEEG channels across subjects for the purposes of group analysis. BackgroundIntracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iEEG power changes that would allow classifying iEEG contacts into functional clusters.MethodsSimultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy whose intracranial electrodes were implanted for pre-surgical evaluation. Pre-processed MEG sensor data was projected to source space. Time delay embedded HMM was then applied to MEG time series. At the same time, iEEG time series were analyzed with time-frequency decomposition to obtain spectral power changes with time. To relate MEG and iEEG results, correlations were computed between HMM probability time courses of state activation and iEEG power time course from the mid contact pair for each electrode in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Association of iEEG electrodes with HMM states based on significant correlations was compared to that based on the distance to peaks in subject-specific state topographies.ResultsFive HMM states were inferred from MEG. Two of them corresponded to the left and the right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. All the electrodes had significant correlations with at least one of the states (p < 0.05 uncorrected) and for 27/50 electrodes these survived within-subject FDR correction (q < 0.05). These correlations peaked in the theta/alpha band. There was a highly significant dependence between the association of states and electrodes based on functional correlations and that based on spatial proximity (p = 5.6e−6,χ2 test for independence). Despite the potentially atypical functional anatomy and physiological abnormalities related to epilepsy, HMM model estimated from the patient group was very similar to that estimated from healthy subjects.ConclusionEpilepsy does not preclude HMM analysis of interictal data. The resulting group functional states are highly similar to those reported for healthy controls. Power changes recorded with iEEG correlate with HMM state time courses in the alpha-theta band and the presence of this correlation can be related to the spatial location of electrode contacts close to the individual peaks of the corresponding state topographies. Thus, the hypothesized relation between iEEG contacts and HMM states exists and HMM could be further explored as a method for identifying comparable iEEG channels across subjects for the purposes of group analysis. • We applied Hidden Markov Model (HMM) to concurrent MEG and intracranial EEG (iEEG). • Epilepsy cohort HMM analysis yielded states similar to those of healthy subjects. • iEEG power correlated with the time course of HMM states. • Functional clusters of iEEG electrodes agreed with those based on spatial location. • Our pipeline can be used for group analysis of concurrent MEG and invasive data. •We applied Hidden Markov Model (HMM) to concurrent MEG and intracranial EEG (iEEG).•Epilepsy cohort HMM analysis yielded states similar to those of healthy subjects.•iEEG power correlated with the time course of HMM states.•Functional clusters of iEEG electrodes agreed with those based on spatial location.•Our pipeline can be used for group analysis of concurrent MEG and invasive data. Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iEEG power changes that would allow classifying iEEG contacts into functional clusters. Simultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy whose intracranial electrodes were implanted for pre-surgical evaluation. Pre-processed MEG sensor data was projected to source space. Time delay embedded HMM was then applied to MEG time series. At the same time, iEEG time series were analyzed with time-frequency decomposition to obtain spectral power changes with time. To relate MEG and iEEG results, correlations were computed between HMM probability time courses of state activation and iEEG power time course from the mid contact pair for each electrode in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Association of iEEG electrodes with HMM states based on significant correlations was compared to that based on the distance to peaks in subject-specific state topographies. Five HMM states were inferred from MEG. Two of them corresponded to the left and the right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. All the electrodes had significant correlations with at least one of the states (p < 0.05 uncorrected) and for 27/50 electrodes these survived within-subject FDR correction (q < 0.05). These correlations peaked in the theta/alpha band. There was a highly significant dependence between the association of states and electrodes based on functional correlations and that based on spatial proximity (p = 5.6e−6,χ2 test for independence). Despite the potentially atypical functional anatomy and physiological abnormalities related to epilepsy, HMM model estimated from the patient group was very similar to that estimated from healthy subjects. Epilepsy does not preclude HMM analysis of interictal data. The resulting group functional states are highly similar to those reported for healthy controls. Power changes recorded with iEEG correlate with HMM state time courses in the alpha-theta band and the presence of this correlation can be related to the spatial location of electrode contacts close to the individual peaks of the corresponding state topographies. Thus, the hypothesized relation between iEEG contacts and HMM states exists and HMM could be further explored as a method for identifying comparable iEEG channels across subjects for the purposes of group analysis. Background: Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iEEG power changes that would allow classifying iEEG contacts into functional clusters. Methods: Simultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy whose intracranial electrodes were implanted for pre-surgical evaluation. Pre-processed MEG sensor data was projected to source space. Time delay embedded HMM was then applied to MEG time series. At the same time, iEEG time series were analyzed with time-frequency decomposition to obtain spectral power changes with time. To relate MEG and iEEG results, correlations were computed between HMM probability time courses of state activation and iEEG power time course from the mid contact pair for each electrode in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Association of iEEG electrodes with HMM states based on significant correlations was compared to that based on the distance to peaks in subject-specific state topographies. Results: Five HMM states were inferred from MEG. Two of them corresponded to the left and the right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. All the electrodes had significant correlations with at least one of the states (p < 0.05 uncorrected) and for 27/50 electrodes these survived within-subject FDR correction (q < 0.05). These correlations peaked in the theta/alpha band. There was a highly significant dependence between the association of states and electrodes based on functional correlations and that based on spatial proximity (p = 5.6e−6,χ2 test for independence). Despite the potentially atypical functional anatomy and physiological abnormalities related to epilepsy, HMM model estimated from the patient group was very similar to that estimated from healthy subjects. Conclusion: Epilepsy does not preclude HMM analysis of interictal data. The resulting group functional states are highly similar to those reported for healthy controls. Power changes recorded with iEEG correlate with HMM state time courses in the alpha-theta band and the presence of this correlation can be related to the spatial location of electrode contacts close to the individual peaks of the corresponding state topographies. Thus, the hypothesized relation between iEEG contacts and HMM states exists and HMM could be further explored as a method for identifying comparable iEEG channels across subjects for the purposes of group analysis. Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iEEG power changes that would allow classifying iEEG contacts into functional clusters.BACKGROUNDIntracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iEEG power changes that would allow classifying iEEG contacts into functional clusters.Simultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy whose intracranial electrodes were implanted for pre-surgical evaluation. Pre-processed MEG sensor data was projected to source space. Time delay embedded HMM was then applied to MEG time series. At the same time, iEEG time series were analyzed with time-frequency decomposition to obtain spectral power changes with time. To relate MEG and iEEG results, correlations were computed between HMM probability time courses of state activation and iEEG power time course from the mid contact pair for each electrode in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Association of iEEG electrodes with HMM states based on significant correlations was compared to that based on the distance to peaks in subject-specific state topographies.METHODSSimultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy whose intracranial electrodes were implanted for pre-surgical evaluation. Pre-processed MEG sensor data was projected to source space. Time delay embedded HMM was then applied to MEG time series. At the same time, iEEG time series were analyzed with time-frequency decomposition to obtain spectral power changes with time. To relate MEG and iEEG results, correlations were computed between HMM probability time courses of state activation and iEEG power time course from the mid contact pair for each electrode in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Association of iEEG electrodes with HMM states based on significant correlations was compared to that based on the distance to peaks in subject-specific state topographies.Five HMM states were inferred from MEG. Two of them corresponded to the left and the right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. All the electrodes had significant correlations with at least one of the states (p < 0.05 uncorrected) and for 27/50 electrodes these survived within-subject FDR correction (q < 0.05). These correlations peaked in the theta/alpha band. There was a highly significant dependence between the association of states and electrodes based on functional correlations and that based on spatial proximity (p = 5.6e-6,χ2 test for independence). Despite the potentially atypical functional anatomy and physiological abnormalities related to epilepsy, HMM model estimated from the patient group was very similar to that estimated from healthy subjects.RESULTSFive HMM states were inferred from MEG. Two of them corresponded to the left and the right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. All the electrodes had significant correlations with at least one of the states (p < 0.05 uncorrected) and for 27/50 electrodes these survived within-subject FDR correction (q < 0.05). These correlations peaked in the theta/alpha band. There was a highly significant dependence between the association of states and electrodes based on functional correlations and that based on spatial proximity (p = 5.6e-6,χ2 test for independence). Despite the potentially atypical functional anatomy and physiological abnormalities related to epilepsy, HMM model estimated from the patient group was very similar to that estimated from healthy subjects.Epilepsy does not preclude HMM analysis of interictal data. The resulting group functional states are highly similar to those reported for healthy controls. Power changes recorded with iEEG correlate with HMM state time courses in the alpha-theta band and the presence of this correlation can be related to the spatial location of electrode contacts close to the individual peaks of the corresponding state topographies. Thus, the hypothesized relation between iEEG contacts and HMM states exists and HMM could be further explored as a method for identifying comparable iEEG channels across subjects for the purposes of group analysis.CONCLUSIONEpilepsy does not preclude HMM analysis of interictal data. The resulting group functional states are highly similar to those reported for healthy controls. Power changes recorded with iEEG correlate with HMM state time courses in the alpha-theta band and the presence of this correlation can be related to the spatial location of electrode contacts close to the individual peaks of the corresponding state topographies. Thus, the hypothesized relation between iEEG contacts and HMM states exists and HMM could be further explored as a method for identifying comparable iEEG channels across subjects for the purposes of group analysis. |
| ArticleNumber | 117923 |
| Author | Litvak, Vladimir Woolrich, Mark Zhang, Siqi Vivekananda, Umesh Liu, Wei Cao, Chunyan Quinn, Andrew Sun, Bomin Lu, Qing Zhan, Shikun |
| Author_xml | – sequence: 1 givenname: Siqi surname: Zhang fullname: Zhang, Siqi organization: Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China – sequence: 2 givenname: Chunyan surname: Cao fullname: Cao, Chunyan organization: Department of Neurosurgery, affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China – sequence: 3 givenname: Andrew surname: Quinn fullname: Quinn, Andrew organization: Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, UK – sequence: 4 givenname: Umesh surname: Vivekananda fullname: Vivekananda, Umesh organization: Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK – sequence: 5 givenname: Shikun surname: Zhan fullname: Zhan, Shikun organization: Department of Neurosurgery, affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China – sequence: 6 givenname: Wei surname: Liu fullname: Liu, Wei organization: Department of Neurosurgery, affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China – sequence: 7 givenname: Bomin surname: Sun fullname: Sun, Bomin organization: Department of Neurosurgery, affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China – sequence: 8 givenname: Mark surname: Woolrich fullname: Woolrich, Mark organization: Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, UK – sequence: 9 givenname: Qing orcidid: 0000-0001-7717-391X surname: Lu fullname: Lu, Qing email: luq@seu.edu.cn organization: Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China – sequence: 10 givenname: Vladimir orcidid: 0000-0001-8535-7452 surname: Litvak fullname: Litvak, Vladimir email: v.litvak@ucl.ac.uk organization: Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33662572$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_neuroimage_2024_120992 crossref_primary_10_3390_brainsci14050507 crossref_primary_10_1016_j_ejpe_2022_09_001 crossref_primary_10_1016_j_jpsychires_2022_03_010 crossref_primary_10_1177_09544119221092503 crossref_primary_10_1016_j_neuroimage_2023_120342 crossref_primary_10_1155_2022_4989947 crossref_primary_10_3390_app12105259 crossref_primary_10_1007_s11571_024_10099_9 crossref_primary_10_3390_bioengineering11080773 |
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| Copyright | 2021 The Authors Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved. 2021. The Authors 2021 The Authors 2021 |
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| Keywords | Human Oscillations Resting state Dynamics |
| Language | English |
| License | This is an open access article under the CC BY license. Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). cc-by |
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| Snippet | •We applied Hidden Markov Model (HMM) to concurrent MEG and intracranial EEG (iEEG).•Epilepsy cohort HMM analysis yielded states similar to those of healthy... Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also... BackgroundIntracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures... • We applied Hidden Markov Model (HMM) to concurrent MEG and intracranial EEG (iEEG). • Epilepsy cohort HMM analysis yielded states similar to those of healthy... Background: Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic... |
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| SubjectTerms | Adolescent Adult Brain - physiology Brain research Data Analysis Dynamics EEG Electrocorticography - methods Electrodes Electroencephalography Epilepsy Epilepsy - diagnosis Epilepsy - physiopathology Ethics Female Functional anatomy Human Humans Information sharing Localization Magnetoencephalography Magnetoencephalography - methods Male Markov Chains Middle Aged Oscillations Patients Resting state Seizures Software Theta rhythms Topography Young Adult |
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| Title | Dynamic analysis on simultaneous iEEG-MEG data via hidden Markov model |
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