Journal: Vision

Article Title: Aesthetic Image Statistics Vary with Artistic Genre

doi: 10.3390/vision4010010

Figure Lengend Snippet: Summary of best-fitting PLSR models applied to paintings in different genres (rows).

Article Snippet: Matlab’s ® PLSR function (plsregress) was applied to the image statistics and ratings.

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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’).

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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:

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:

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:

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: