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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 1225 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/mtrf+matlab+toolbox/Control+System+Toolbox/pmc12875487-216-23-24
    Average 96 stars, based on 1225 article reviews
    mtrf matlab toolbox - by Bioz Stars, 2026-09
    96/100 stars

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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

    Control:

    Article Title: Neural Speech Tracking during Selective Attention: A Spatially Realistic Audiovisual Study
    Article Snippet: We estimated multivariate linear Temporal Response Functions (TRFs) using the mTRF MATLAB toolbox , which constitutes a linear transfer function describing the relationship between a particular feature of the stimuli (S) and the neural response (R) recorded when hearing it.

    Article Title: The effect of voice familiarity on attention to speech in a cocktail party scenario.
    Article Snippet: Selective attention to one speaker in multi-talker environments can be affected by the acoustic and semantic properties of speech.. One highly ecological feature of speech that has the potential to assist in selective attention is voice familiarity.. Here, we tested how voice familiarity interacts with selective attention by measuring the neural speech-tracking response to both target and non-target speech in a dichotic listening “Cocktail Party” paradigm.

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

    Article Title: Point-light Talkers: Multisensory Enhancement of Speech Tracking by Co-speech Movement Kinematics.
    Article Snippet: Using the mTRF MATLAB toolbox (Crosse et al., 2016), we applied ridge regression (Hoerle & Kennard, 1970) to predict the speech amplitude envelope from the concurrently recorded EEG data.

    Article Title: An ecological investigation of the effect of background noise on speech processing in a Virtual Classroom
    Article Snippet: To estimate the neural response to the speech we performed speech-tracking analysis on the data, and estimated Temporal Response Functions (TRF’s) using the mTRF MATLAB toolbox ( ).

    Article Title: Neural encoding of musical expectations in a non-human primate.
    Article Snippet: This analysis was carried out using the mTRF Matlab toolbox.33 Each stimulus feature was normalized across time for each melody such that the root mean square of each feature was equal to 1.

    Article Title: Point-light Talkers: Multisensory Enhancement of Speech Tracking by Co-speech Movement Kinematics.
    Article Snippet: The entire stimulus reconstruction analysis was performed using the mTRF MATLAB toolbox (Crosse et al., 2016).

    Preserving:

    Article Title: Neural Speech Tracking during Selective Attention: A Spatially Realistic Audiovisual Study
    Article Snippet: We estimated multivariate linear Temporal Response Functions (TRFs) using the mTRF MATLAB toolbox , which constitutes a linear transfer function describing the relationship between a particular feature of the stimuli (S) and the neural response (R) recorded when hearing it.

    Article Title: The effect of voice familiarity on attention to speech in a cocktail party scenario.
    Article Snippet: Selective attention to one speaker in multi-talker environments can be affected by the acoustic and semantic properties of speech.. One highly ecological feature of speech that has the potential to assist in selective attention is voice familiarity.. Here, we tested how voice familiarity interacts with selective attention by measuring the neural speech-tracking response to both target and non-target speech in a dichotic listening “Cocktail Party” paradigm.

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

    Article Title: Point-light Talkers: Multisensory Enhancement of Speech Tracking by Co-speech Movement Kinematics.
    Article Snippet: Using the mTRF MATLAB toolbox (Crosse et al., 2016), we applied ridge regression (Hoerle & Kennard, 1970) to predict the speech amplitude envelope from the concurrently recorded EEG data.

    Article Title: An ecological investigation of the effect of background noise on speech processing in a Virtual Classroom
    Article Snippet: To estimate the neural response to the speech we performed speech-tracking analysis on the data, and estimated Temporal Response Functions (TRF’s) using the mTRF MATLAB toolbox ( ).

    Article Title: Neural encoding of musical expectations in a non-human primate.
    Article Snippet: This analysis was carried out using the mTRF Matlab toolbox.33 Each stimulus feature was normalized across time for each melody such that the root mean square of each feature was equal to 1.

    Article Title: Point-light Talkers: Multisensory Enhancement of Speech Tracking by Co-speech Movement Kinematics.
    Article Snippet: The entire stimulus reconstruction analysis was performed using the mTRF MATLAB toolbox (Crosse et al., 2016).



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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 <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