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mtrf matlab toolbox  (MathWorks Inc)


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    MathWorks Inc mtrf matlab toolbox
    (A) Experimental paradigm. We analyzed EEG data recorded from 49 sleeping human newborns while being exposed to monophonic piano melodies composed by J. S. Bach (real condition) and control stimuli (shuffled condition). (B) Surprise and entropy. Surprise and entropy associated with each note’s timing (green, St and Et, respectively) and pitch (yellow, Sp and Ep, respectively) were estimated using an unsupervised statistical learning model trained on all stimuli. Dot plots display mean surprise and entropy associated with real and shuffled music, averaged across melodies (left panel), and separately for each melody (right panel). Error bars represent bootstrapped 95% confidence intervals (CI). See . (C) Correlations between stimulus features. Pearson’s correlations ( r values) between the stimulus features: inter-pitch-interval (IPI), inter-onset-interval (IOI), and surprise and entropy associated with timing (St and Et) and pitch (Sp and Ep). See . (D) Analytical approach. Multivariate Temporal Response Function <t>(mTRF)</t> models were fit to describe the forward relationship between multiple stimulus features and the EEG signal. The full TRF model (leftmost panel) <t>included</t> <t>acoustic</t> low-level features (spectral flux, acoustic onset, IOI, and IPI) and high-level features (surprise and entropy of pitch and timing). To assess the unique contribution of each feature (or set of features) to the EEG data, we run reduced models encompassing all variables but with the specified one being randomized in time (yet preserving the note onset times). We then calculated the difference in EEG prediction accuracy (Pearson’s correlations, r ) between the reduced models and the full model (Δr). On the rightmost panel, the light blue circle denotes information of a reduced model, with the variable(s) of interest being randomized. The orange area indicates the unique contribution of the variable of interest that leads to an increase in the explanatory power of the full model (black circle).
    Mtrf Matlab Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1226 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/mtrf+toolbox/Control+System+Toolbox/pmc12875487-216-23-24
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    mtrf matlab toolbox - by Bioz Stars, 2026-09
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    1) Product Images from "Human newborns form musical predictions based on rhythmic but not melodic structure"

    Article Title: Human newborns form musical predictions based on rhythmic but not melodic structure

    Journal: PLOS Biology

    doi: 10.1371/journal.pbio.3003600

    (A) Experimental paradigm. We analyzed EEG data recorded from 49 sleeping human newborns while being exposed to monophonic piano melodies composed by J. S. Bach (real condition) and control stimuli (shuffled condition). (B) Surprise and entropy. Surprise and entropy associated with each note’s timing (green, St and Et, respectively) and pitch (yellow, Sp and Ep, respectively) were estimated using an unsupervised statistical learning model trained on all stimuli. Dot plots display mean surprise and entropy associated with real and shuffled music, averaged across melodies (left panel), and separately for each melody (right panel). Error bars represent bootstrapped 95% confidence intervals (CI). See . (C) Correlations between stimulus features. Pearson’s correlations ( r values) between the stimulus features: inter-pitch-interval (IPI), inter-onset-interval (IOI), and surprise and entropy associated with timing (St and Et) and pitch (Sp and Ep). See . (D) Analytical approach. Multivariate Temporal Response Function (mTRF) models were fit to describe the forward relationship between multiple stimulus features and the EEG signal. The full TRF model (leftmost panel) included acoustic low-level features (spectral flux, acoustic onset, IOI, and IPI) and high-level features (surprise and entropy of pitch and timing). To assess the unique contribution of each feature (or set of features) to the EEG data, we run reduced models encompassing all variables but with the specified one being randomized in time (yet preserving the note onset times). We then calculated the difference in EEG prediction accuracy (Pearson’s correlations, r ) between the reduced models and the full model (Δr). On the rightmost panel, the light blue circle denotes information of a reduced model, with the variable(s) of interest being randomized. The orange area indicates the unique contribution of the variable of interest that leads to an increase in the explanatory power of the full model (black circle).
    Figure Legend Snippet: (A) Experimental paradigm. We analyzed EEG data recorded from 49 sleeping human newborns while being exposed to monophonic piano melodies composed by J. S. Bach (real condition) and control stimuli (shuffled condition). (B) Surprise and entropy. Surprise and entropy associated with each note’s timing (green, St and Et, respectively) and pitch (yellow, Sp and Ep, respectively) were estimated using an unsupervised statistical learning model trained on all stimuli. Dot plots display mean surprise and entropy associated with real and shuffled music, averaged across melodies (left panel), and separately for each melody (right panel). Error bars represent bootstrapped 95% confidence intervals (CI). See . (C) Correlations between stimulus features. Pearson’s correlations ( r values) between the stimulus features: inter-pitch-interval (IPI), inter-onset-interval (IOI), and surprise and entropy associated with timing (St and Et) and pitch (Sp and Ep). See . (D) Analytical approach. Multivariate Temporal Response Function (mTRF) models were fit to describe the forward relationship between multiple stimulus features and the EEG signal. The full TRF model (leftmost panel) included acoustic low-level features (spectral flux, acoustic onset, IOI, and IPI) and high-level features (surprise and entropy of pitch and timing). To assess the unique contribution of each feature (or set of features) to the EEG data, we run reduced models encompassing all variables but with the specified one being randomized in time (yet preserving the note onset times). We then calculated the difference in EEG prediction accuracy (Pearson’s correlations, r ) between the reduced models and the full model (Δr). On the rightmost panel, the light blue circle denotes information of a reduced model, with the variable(s) of interest being randomized. The orange area indicates the unique contribution of the variable of interest that leads to an increase in the explanatory power of the full model (black circle).

    Techniques Used: Control, Preserving

    Related Articles

    other:

    Article Title: Neural response attenuation for shorter inter-onset intervals between sounds in a natural soundscape
    Article Snippet: The neural time series were analyzed using the mTRF toolbox ( ) in MATLAB.

    Article Title: Event-related brain activity in response to partners' speech in natural conversation.
    Article Snippet: The multivariate temporal response function 609 (mTRF) toolbox: a MATLAB toolbox for relating neural signals to continuous stimuli.

    Article Title: Neural speech tracking contribution of lip movements predicts behavioral deterioration when the speaker's mouth is occluded
    Article Snippet: The multivariate temporal 789 response function (mTRF) toolbox: A Matlab toolbox for relating neural signals to 790 continuous stimuli.

    Article Title: Neural Speech-Tracking During Selective Attention: A Spatially Realistic Audiovisual Study
    Article Snippet: We 242 estimated multivariate linear Temporal Response Functions (TRFs) using the mTRF MATLAB 243 toolbox (Crosse et al., 2016), which constitutes a linear transfer function describing the 244 eN eu ro Ac ce pte d M a us cri pt 9 relationship between a particular feature of the stimuli (S) and the neural response (R) 245 recorded when hearing it.

    Article Title: Temporal Prediction through Integration of Probability Distributions of Event Timings at Multiple Levels
    Article Snippet: The analysis was conducted using the MATLAB-based mTRF toolbox.

    Article Title: Effects of Age and Cognitive Functions on the Neural Tracking of Speech in Noise
    Article Snippet: The preprocessing of all data analysis was conducted using EEGLAB and the mTRF toolbox ( ) in MATLAB (v9.5.0 R2018b, The MathWorks, Inc., Natick, MA, USA).

    Article Title: Exploring Relevant Features for EEG-Based Investigation of Sound Perception in Naturalistic Soundscapes
    Article Snippet: To analyze the neural time series, we employed the mTRF toolbox developed by in MATLAB.

    Plasmid Preparation:

    Article Title: Exploring Relevant Features for EEG-Based Investigation of Sound Perception in Naturalistic Soundscapes
    Article Snippet: .. AC = acoustic features, SI = sound identity condition and another vector depicting the alarm tone onsets in the wide condition.308 2.3 mTRF309 To analyze the neural time series, we employed the mTRF toolbox developed by Crosse et310 al. (2016) in MATLAB. ..



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    (A) Experimental paradigm. We analyzed EEG data recorded from 49 sleeping human newborns while being exposed to monophonic piano melodies composed by J. S. Bach (real condition) and control stimuli (shuffled condition). (B) Surprise and entropy. Surprise and entropy associated with each note’s timing (green, St and Et, respectively) and pitch (yellow, Sp and Ep, respectively) were estimated using an unsupervised statistical learning model trained on all stimuli. Dot plots display mean surprise and entropy associated with real and shuffled music, averaged across melodies (left panel), and separately for each melody (right panel). Error bars represent bootstrapped 95% confidence intervals (CI). See . (C) Correlations between stimulus features. Pearson’s correlations ( r values) between the stimulus features: inter-pitch-interval (IPI), inter-onset-interval (IOI), and surprise and entropy associated with timing (St and Et) and pitch (Sp and Ep). See . (D) Analytical approach. Multivariate Temporal Response Function <t>(mTRF)</t> models were fit to describe the forward relationship between multiple stimulus features and the EEG signal. The full TRF model (leftmost panel) <t>included</t> <t>acoustic</t> low-level features (spectral flux, acoustic onset, IOI, and IPI) and high-level features (surprise and entropy of pitch and timing). To assess the unique contribution of each feature (or set of features) to the EEG data, we run reduced models encompassing all variables but with the specified one being randomized in time (yet preserving the note onset times). We then calculated the difference in EEG prediction accuracy (Pearson’s correlations, r ) between the reduced models and the full model (Δr). On the rightmost panel, the light blue circle denotes information of a reduced model, with the variable(s) of interest being randomized. The orange area indicates the unique contribution of the variable of interest that leads to an increase in the explanatory power of the full model (black circle).
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    (A) Experimental paradigm. We analyzed EEG data recorded from 49 sleeping human newborns while being exposed to monophonic piano melodies composed by J. S. Bach (real condition) and control stimuli (shuffled condition). (B) Surprise and entropy. Surprise and entropy associated with each note’s timing (green, St and Et, respectively) and pitch (yellow, Sp and Ep, respectively) were estimated using an unsupervised statistical learning model trained on all stimuli. Dot plots display mean surprise and entropy associated with real and shuffled music, averaged across melodies (left panel), and separately for each melody (right panel). Error bars represent bootstrapped 95% confidence intervals (CI). See . (C) Correlations between stimulus features. Pearson’s correlations ( r values) between the stimulus features: inter-pitch-interval (IPI), inter-onset-interval (IOI), and surprise and entropy associated with timing (St and Et) and pitch (Sp and Ep). See . (D) Analytical approach. Multivariate Temporal Response Function (mTRF) models were fit to describe the forward relationship between multiple stimulus features and the EEG signal. The full TRF model (leftmost panel) included acoustic low-level features (spectral flux, acoustic onset, IOI, and IPI) and high-level features (surprise and entropy of pitch and timing). To assess the unique contribution of each feature (or set of features) to the EEG data, we run reduced models encompassing all variables but with the specified one being randomized in time (yet preserving the note onset times). We then calculated the difference in EEG prediction accuracy (Pearson’s correlations, r ) between the reduced models and the full model (Δr). On the rightmost panel, the light blue circle denotes information of a reduced model, with the variable(s) of interest being randomized. The orange area indicates the unique contribution of the variable of interest that leads to an increase in the explanatory power of the full model (black circle).

    Journal: PLOS Biology

    Article Title: Human newborns form musical predictions based on rhythmic but not melodic structure

    doi: 10.1371/journal.pbio.3003600

    Figure Lengend Snippet: (A) Experimental paradigm. We analyzed EEG data recorded from 49 sleeping human newborns while being exposed to monophonic piano melodies composed by J. S. Bach (real condition) and control stimuli (shuffled condition). (B) Surprise and entropy. Surprise and entropy associated with each note’s timing (green, St and Et, respectively) and pitch (yellow, Sp and Ep, respectively) were estimated using an unsupervised statistical learning model trained on all stimuli. Dot plots display mean surprise and entropy associated with real and shuffled music, averaged across melodies (left panel), and separately for each melody (right panel). Error bars represent bootstrapped 95% confidence intervals (CI). See . (C) Correlations between stimulus features. Pearson’s correlations ( r values) between the stimulus features: inter-pitch-interval (IPI), inter-onset-interval (IOI), and surprise and entropy associated with timing (St and Et) and pitch (Sp and Ep). See . (D) Analytical approach. Multivariate Temporal Response Function (mTRF) models were fit to describe the forward relationship between multiple stimulus features and the EEG signal. The full TRF model (leftmost panel) included acoustic low-level features (spectral flux, acoustic onset, IOI, and IPI) and high-level features (surprise and entropy of pitch and timing). To assess the unique contribution of each feature (or set of features) to the EEG data, we run reduced models encompassing all variables but with the specified one being randomized in time (yet preserving the note onset times). We then calculated the difference in EEG prediction accuracy (Pearson’s correlations, r ) between the reduced models and the full model (Δr). On the rightmost panel, the light blue circle denotes information of a reduced model, with the variable(s) of interest being randomized. The orange area indicates the unique contribution of the variable of interest that leads to an increase in the explanatory power of the full model (black circle).

    Article Snippet: We employed Temporal Response Functions (TRF) to model EEG responses to the continuous acoustic and musical features of the presented stimuli using the mTRF MATLAB toolbox [ ].

    Techniques: Control, Preserving