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hidden markov model - multivariate autoregressive (hmm-mar) matlab toolbox  (MathWorks Inc)


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    MathWorks Inc hidden markov model - multivariate autoregressive (hmm-mar) matlab toolbox
    Hidden Markov Model Multivariate Autoregressive (Hmm Mar) Matlab Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/hidden markov model - multivariate autoregressive (hmm-mar) matlab toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    hidden markov model - multivariate autoregressive (hmm-mar) matlab toolbox - by Bioz Stars, 2026-04
    90/100 stars

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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 ) <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: