Journal: Developmental Cognitive Neuroscience

Article Title: Spectral parameterization for studying neurodevelopment: How and why

doi: 10.1016/j.dcn.2022.101073

Figure Lengend Snippet: Brief overview of key settings for the specparam algorithm.

Article Snippet: Abbreviated version of the 03-R_groupPSDs.Rmd script that parameterizes multiple power spectra using specparam in R Studio.

Techniques:

Journal: Developmental Cognitive Neuroscience

Article Title: Spectral parameterization for studying neurodevelopment: How and why

doi: 10.1016/j.dcn.2022.101073

Figure Lengend Snippet: Abbreviated version of the 01-IndividualPSD.ipynb script for parameterizing individual power spectrum using specparam in Jupyter Notebook ( A ). See the GitHub repository for full annotated script. Results of the specparam fitting of EEG data from a single child, recorded during an eyes-closed resting state, is presented in B . CF = center frequency of identified peak. PW = power of identified peak above the aperiodic signal. BW = band width of identified peak.

Article Snippet: Abbreviated version of the 03-R_groupPSDs.Rmd script that parameterizes multiple power spectra using specparam in R Studio.

Techniques:

Journal: Developmental Cognitive Neuroscience

Article Title: Spectral parameterization for studying neurodevelopment: How and why

doi: 10.1016/j.dcn.2022.101073

Figure Lengend Snippet: Abbreviated version of the 03-R_groupPSDs.Rmd script that parameterizes multiple power spectra using specparam in R Studio. See the GitHub repository for full annotated script.

Article Snippet: Abbreviated version of the 03-R_groupPSDs.Rmd script that parameterizes multiple power spectra using specparam in R Studio.

Techniques:

Journal: Developmental Cognitive Neuroscience

Article Title: Spectral parameterization for studying neurodevelopment: How and why

doi: 10.1016/j.dcn.2022.101073

Figure Lengend Snippet: Histograms for variance explained (R^2) and mean absolute error (MAE) for the full sample, recorded during an eyes-open resting state ( A ). Mean error per frequency, as well as standard deviation in error per frequency (blue shading), are presented in B . In this condition, the 3 Hz bin had the highest mean error and largest standard deviation in error, suggesting possible misfit at the lower end of the examined frequency range. Further consideration about specparam settings may be needed. C and D depict two fit models that were flagged as potentially being overfit (MAE < 0.025) and underfit (MAE > 0.100), respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Article Snippet: Abbreviated version of the 03-R_groupPSDs.Rmd script that parameterizes multiple power spectra using specparam in R Studio.

Techniques: Standard Deviation