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hmm mar toolbox in matlab  (MathWorks Inc)


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    MathWorks Inc hmm mar toolbox in matlab
    Hmm Mar Toolbox In Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2341 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/hmm-mar+toolbox/Database+Toolbox/pm40610759-210-11-14
    Average 96 stars, based on 2341 article reviews
    hmm mar toolbox in matlab - by Bioz Stars, 2026-10
    96/100 stars

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    Related Articles

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    Article Title: A Depression-Risk Mental Pattern Identified by Hidden Markov Model in Undergraduates
    Article Snippet: Estimating the HMM and the observed variable covariance matrix and the standard value of each state can be calculated by the HMM-MAR [ , , ] toolbox in MATLAB R2020a.

    Article Title: Characterizing dynamic functional connectivity in congenitally blind people using Hidden Markov Models
    Article Snippet: In this study, we adopted a methodology previously outlined in , to estimate the temporal dynamic of hidden states by leveraging the HMM-MAR toolbox ( ) which is available in MATLAB via the public repository ( https://github.com/OHBA-analysis/HMM-MAR ).

    Article Title: Inferring Brain State Dynamics Underlying Naturalistic Stimuli Evoked Emotion Changes With dHA-HMM.
    Article Snippet: The brain functional mechanisms underlying emotional changes have been primarily studied based on the traditional task design with discrete and simple stimuli.. However, the brain state transitions when exposed to continuous and naturalistic stimuli with rich affection variations remain poorly understood.. This study proposes a dynamic hyperalignment algorithm (dHA) to functionally align the inter-subject neural activity.

    Article Title: Spontaneous network activity <35 ​Hz accounts for variability in stimulus-induced gamma responses
    Article Snippet: Data were preprocessed and analysed using the HMM-MAR ( ) and Fieldtrip ( ) toolboxes for Matlab (The Mathworks).

    Article Title: Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models
    Article Snippet: The HMM was inferred using the HMM‐MAR toolbox (Vidaurre et al., ), available in MATLAB in a public repository ( https://github.com/OHBA-analysis/HMM-MAR ).

    Article Title: Data and model considerations for estimating time-varying functional connectivity in fMRI
    Article Snippet: The number of free parameters per state D F in a HMM with K states, each defined by a full covariance matrix but without modeling the mean, and N parcels can be computed as D F = K * ( K − 1 ) + ( K − 1 ) + K * N * ( N + 1 ) 2 K . We implemented the model using the HMM-MAR toolbox available at https://github.com/OHBA-analysis/HMM-MAR in MATLAB ( ).

    Article Title: Neonatal cortical activity organizes into transient network states that are affected by vigilance states and brain injury.
    Article Snippet: All the computations related to TDE-HMM were done using HMM-MAR toolbox (https://github. com/OHBA-analysis/HMM-MAR) implemented in MATLAB.



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    ( A ) Participants slept inside a scanner from ~23:00 to ~07:00 for two consecutive nights, with concurrent EEG-fMRI recording. During each night, the fMRI experiments were intermittently disrupted by either acoustical arousals (eight random arousals) or spontaneous awakenings. Sleep stages and slow wave density were derived from EEG signals alone. ( B ) <t>Hidden</t> <t>Markov</t> <t>model</t> <t>(HMM)</t> was trained on the principal components of fMRI signals of night 2. Then the identified HMM states were generalized to night 1 fMRI signals. Finally, we studied the state-related variations in fMRI activation, FC patterns, and EEG measures. Notes: EEG, electroencephalographic; TR: repetition time; FC, functional connectivity; ROI, region of interest; PCA, principal component analysis.
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    ( A ) Participants slept inside a scanner from ~23:00 to ~07:00 for two consecutive nights, with concurrent EEG-fMRI recording. During each night, the fMRI experiments were intermittently disrupted by either acoustical arousals (eight random arousals) or spontaneous awakenings. Sleep stages and slow wave density were derived from EEG signals alone. ( B ) Hidden Markov model (HMM) was trained on the principal components of fMRI signals of night 2. Then the identified HMM states were generalized to night 1 fMRI signals. Finally, we studied the state-related variations in fMRI activation, FC patterns, and EEG measures. Notes: EEG, electroencephalographic; TR: repetition time; FC, functional connectivity; ROI, region of interest; PCA, principal component analysis.

    Journal: eLife

    Article Title: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

    doi: 10.7554/eLife.98739

    Figure Lengend Snippet: ( A ) Participants slept inside a scanner from ~23:00 to ~07:00 for two consecutive nights, with concurrent EEG-fMRI recording. During each night, the fMRI experiments were intermittently disrupted by either acoustical arousals (eight random arousals) or spontaneous awakenings. Sleep stages and slow wave density were derived from EEG signals alone. ( B ) Hidden Markov model (HMM) was trained on the principal components of fMRI signals of night 2. Then the identified HMM states were generalized to night 1 fMRI signals. Finally, we studied the state-related variations in fMRI activation, FC patterns, and EEG measures. Notes: EEG, electroencephalographic; TR: repetition time; FC, functional connectivity; ROI, region of interest; PCA, principal component analysis.

    Article Snippet: We employed a Gaussian HMM using the Matlab toolbox HMM-MAR v1.0 ( https://github.com/OHBA-analysis/HMM-MAR , copy archived at ), where each state was modeled as a multivariate normal distribution encompassing both first-order statistics (mean activity) and second-order statistics (covariance matrix).

    Techniques: Derivative Assay, Activation Assay, Functional Assay

    State timecourse of Hidden Markov model (HMM) states and its associations with polysomnography (PSG) stages, variation in photoplethysmography (PPG) amplitude, and variations in RespRVT signals of an example run.

    Journal: eLife

    Article Title: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

    doi: 10.7554/eLife.98739

    Figure Lengend Snippet: State timecourse of Hidden Markov model (HMM) states and its associations with polysomnography (PSG) stages, variation in photoplethysmography (PPG) amplitude, and variations in RespRVT signals of an example run.

    Article Snippet: We employed a Gaussian HMM using the Matlab toolbox HMM-MAR v1.0 ( https://github.com/OHBA-analysis/HMM-MAR , copy archived at ), where each state was modeled as a multivariate normal distribution encompassing both first-order statistics (mean activity) and second-order statistics (covariance matrix).

    Techniques:

    State timecourse of Hidden Markov model (HMM) states and its associations with polysomnography (PSG) stages, variation in photoplethysmography (PPG) amplitude, and variations in RespRVT signals of a second example run.

    Journal: eLife

    Article Title: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

    doi: 10.7554/eLife.98739

    Figure Lengend Snippet: State timecourse of Hidden Markov model (HMM) states and its associations with polysomnography (PSG) stages, variation in photoplethysmography (PPG) amplitude, and variations in RespRVT signals of a second example run.

    Article Snippet: We employed a Gaussian HMM using the Matlab toolbox HMM-MAR v1.0 ( https://github.com/OHBA-analysis/HMM-MAR , copy archived at ), where each state was modeled as a multivariate normal distribution encompassing both first-order statistics (mean activity) and second-order statistics (covariance matrix).

    Techniques:

    The error bars represent the standard error of the mean. Panel ( A ) free energy; Panel ( B ) maximum Occupancy (percentage); Panel ( C ) median Occupancy (percentage); Panel ( D ) Wilk’s Λ; Panel ( E ) mean Hidden Markov model (HMM) state Lifetime (TR, 3 s).

    Journal: eLife

    Article Title: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

    doi: 10.7554/eLife.98739

    Figure Lengend Snippet: The error bars represent the standard error of the mean. Panel ( A ) free energy; Panel ( B ) maximum Occupancy (percentage); Panel ( C ) median Occupancy (percentage); Panel ( D ) Wilk’s Λ; Panel ( E ) mean Hidden Markov model (HMM) state Lifetime (TR, 3 s).

    Article Snippet: We employed a Gaussian HMM using the Matlab toolbox HMM-MAR v1.0 ( https://github.com/OHBA-analysis/HMM-MAR , copy archived at ), where each state was modeled as a multivariate normal distribution encompassing both first-order statistics (mean activity) and second-order statistics (covariance matrix).

    Techniques: