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pls function plsregress.m  (MathWorks Inc)


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    MathWorks Inc pls function plsregress.m
    The prediction of chronological age <t>with</t> <t>EEG‐age</t> via <t>PLS</t> analysis of EEG spectra. (a) the percentage variance in chronological age accounted for by the number of components; (b) The factor weights of the two significant factors (i.e., latent variables) by frequency; (c) The two‐dimensional β‐weights by frequency; (d) Chronological age (True Age) by EEG‐age (PLS Estimated Age); (e) The R‐PLS optimal β‐weights by frequency; (f) Chronological age (True Age) by EEG‐age (R‐PLS Estimated Age).
    Pls Function Plsregress.M, 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/pls function plsregress.m/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    pls function plsregress.m - by Bioz Stars, 2026-04
    90/100 stars

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    1) Product Images from "Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning"

    Article Title: Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

    Journal: Psychophysiology

    doi: 10.1111/psyp.70033

    The prediction of chronological age with EEG‐age via PLS analysis of EEG spectra. (a) the percentage variance in chronological age accounted for by the number of components; (b) The factor weights of the two significant factors (i.e., latent variables) by frequency; (c) The two‐dimensional β‐weights by frequency; (d) Chronological age (True Age) by EEG‐age (PLS Estimated Age); (e) The R‐PLS optimal β‐weights by frequency; (f) Chronological age (True Age) by EEG‐age (R‐PLS Estimated Age).
    Figure Legend Snippet: The prediction of chronological age with EEG‐age via PLS analysis of EEG spectra. (a) the percentage variance in chronological age accounted for by the number of components; (b) The factor weights of the two significant factors (i.e., latent variables) by frequency; (c) The two‐dimensional β‐weights by frequency; (d) Chronological age (True Age) by EEG‐age (PLS Estimated Age); (e) The R‐PLS optimal β‐weights by frequency; (f) Chronological age (True Age) by EEG‐age (R‐PLS Estimated Age).

    Techniques Used:

    The prediction of chronological age with EEG‐age via M‐PLS analysis of EEG log2(amplitude) spectra. (a, d and g) show the factor loadings by frequency; (b, e and h) show factor weightings by topography; (c, f and i) show the relationships between each factor and chronological age; (j) shows the prediction of chronological age with the three‐factor model of EEG‐age.
    Figure Legend Snippet: The prediction of chronological age with EEG‐age via M‐PLS analysis of EEG log2(amplitude) spectra. (a, d and g) show the factor loadings by frequency; (b, e and h) show factor weightings by topography; (c, f and i) show the relationships between each factor and chronological age; (j) shows the prediction of chronological age with the three‐factor model of EEG‐age.

    Techniques Used:

    Differences between correlations of chronological age with brain age for PAF age and PLS EEG age methods.
    Figure Legend Snippet: Differences between correlations of chronological age with brain age for PAF age and PLS EEG age methods.

    Techniques Used:

    Correlations of M‐PAF age, PLS EEG age, and chronological age (CA) with QMCI.
    Figure Legend Snippet: Correlations of M‐PAF age, PLS EEG age, and chronological age (CA) with QMCI.

    Techniques Used:

    Differences between correlations of age with QMCI for M‐PAF age, PLS EEG age, and chronological age (CA), when accounting for NART‐IQ.
    Figure Legend Snippet: Differences between correlations of age with QMCI for M‐PAF age, PLS EEG age, and chronological age (CA), when accounting for NART‐IQ.

    Techniques Used:



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    The prediction of chronological age with EEG‐age via PLS analysis of EEG spectra. (a) the percentage variance in chronological age accounted for by the number of components; (b) The factor weights of the two significant factors (i.e., latent variables) by frequency; (c) The two‐dimensional β‐weights by frequency; (d) Chronological age (True Age) by EEG‐age (PLS Estimated Age); (e) The R‐PLS optimal β‐weights by frequency; (f) Chronological age (True Age) by EEG‐age (R‐PLS Estimated Age).

    Journal: Psychophysiology

    Article Title: Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

    doi: 10.1111/psyp.70033

    Figure Lengend Snippet: The prediction of chronological age with EEG‐age via PLS analysis of EEG spectra. (a) the percentage variance in chronological age accounted for by the number of components; (b) The factor weights of the two significant factors (i.e., latent variables) by frequency; (c) The two‐dimensional β‐weights by frequency; (d) Chronological age (True Age) by EEG‐age (PLS Estimated Age); (e) The R‐PLS optimal β‐weights by frequency; (f) Chronological age (True Age) by EEG‐age (R‐PLS Estimated Age).

    Article Snippet: The ability of the broad EEG power spectrum of the 0.1 to 45 Hz range to predict chronological age (i.e., EEG‐age) was assessed using PLS (MATLAB function ‘plsregress.m’).

    Techniques:

    The prediction of chronological age with EEG‐age via M‐PLS analysis of EEG log2(amplitude) spectra. (a, d and g) show the factor loadings by frequency; (b, e and h) show factor weightings by topography; (c, f and i) show the relationships between each factor and chronological age; (j) shows the prediction of chronological age with the three‐factor model of EEG‐age.

    Journal: Psychophysiology

    Article Title: Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

    doi: 10.1111/psyp.70033

    Figure Lengend Snippet: The prediction of chronological age with EEG‐age via M‐PLS analysis of EEG log2(amplitude) spectra. (a, d and g) show the factor loadings by frequency; (b, e and h) show factor weightings by topography; (c, f and i) show the relationships between each factor and chronological age; (j) shows the prediction of chronological age with the three‐factor model of EEG‐age.

    Article Snippet: The ability of the broad EEG power spectrum of the 0.1 to 45 Hz range to predict chronological age (i.e., EEG‐age) was assessed using PLS (MATLAB function ‘plsregress.m’).

    Techniques:

    Differences between correlations of chronological age with brain age for PAF age and PLS EEG age methods.

    Journal: Psychophysiology

    Article Title: Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

    doi: 10.1111/psyp.70033

    Figure Lengend Snippet: Differences between correlations of chronological age with brain age for PAF age and PLS EEG age methods.

    Article Snippet: The ability of the broad EEG power spectrum of the 0.1 to 45 Hz range to predict chronological age (i.e., EEG‐age) was assessed using PLS (MATLAB function ‘plsregress.m’).

    Techniques:

    Correlations of M‐PAF age, PLS EEG age, and chronological age (CA) with QMCI.

    Journal: Psychophysiology

    Article Title: Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

    doi: 10.1111/psyp.70033

    Figure Lengend Snippet: Correlations of M‐PAF age, PLS EEG age, and chronological age (CA) with QMCI.

    Article Snippet: The ability of the broad EEG power spectrum of the 0.1 to 45 Hz range to predict chronological age (i.e., EEG‐age) was assessed using PLS (MATLAB function ‘plsregress.m’).

    Techniques:

    Differences between correlations of age with QMCI for M‐PAF age, PLS EEG age, and chronological age (CA), when accounting for NART‐IQ.

    Journal: Psychophysiology

    Article Title: Estimating Chronological Age From the Electrical Activity of the Brain: How EEG ‐Age Can Be Used as a Marker of General Brain Functioning

    doi: 10.1111/psyp.70033

    Figure Lengend Snippet: Differences between correlations of age with QMCI for M‐PAF age, PLS EEG age, and chronological age (CA), when accounting for NART‐IQ.

    Article Snippet: The ability of the broad EEG power spectrum of the 0.1 to 45 Hz range to predict chronological age (i.e., EEG‐age) was assessed using PLS (MATLAB function ‘plsregress.m’).

    Techniques: