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spectral parameterization (specparam) toolbox (MathWorks Inc)

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MathWorks Inc spectral parameterization (specparam) toolbox
Spectral Parameterization (Specparam) Toolbox, 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/product/specparam+(++%3B/pmc10312609-116-18-32
Average 90 stars, based on 1 article reviews

spectral parameterization (specparam) toolbox - by Bioz Stars, 2026-10

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Article Title: Supporting Information Versatile Para-Substituted Pyridine Lanthanide Coordination Complexes Allow Late Stage Tailoring of Complex Function
Article Snippet: All values were obtained using the custom functions with the Curve Fitting Toolbox within MATLAB R2020b.

Article Title: Distinct modulation of microsaccades in motor planning and covert attention.
Article Snippet: We used a MATLAB custom made toolbox for saccade detection.

Article Title: Dysregulated neural coding in the vagus nerve during long sepsis
Article Snippet: Spikes were sorted using t-distributed stochastic neighbor embedding (t-SNE) with the Toolbox for Dimensionality Reduction ( https://lvdmaaten.github.io/drtoolbox/ ) in MATLAB.

Article Title: Evaluation of individual and combined effects of microplastics and naphthalene on aquatic sediment: Disturbance of carbon and microbial dynamics.
Article Snippet: • Microplastics promote NPT adsorption and DOM humification.. • Microplastic increased the NPT-induced antioxidant activity of plants.. • The increase of microplastic concentration caused higher biological toxicity.

Article Title: Perinatal serotonin signalling dynamically influences the development of cortical GABAergic circuits with consequences for lifelong sensory encoding.
Article Snippet: Analysis of LSPS data was performed with a customised MATLAB (R2020a, MathWorks) toolbox.

Article Title: Supporting Information for Enhanced metamorphic CO2 release on the Proterozoic Earth
Article Snippet: Input variables with flat distributions variable minimum maximum mass fraction clay in siliceous dolostone or limestone 0.01 0.20 mass fraction carbonate in calcitic siliciclastic rocks 0.01 0.20 minimum number of observations per time bin 50 1000 Table S4 Best-fit coefficients for equation S4 from Matlab Curve Fitting toolbox a b c d e R2 calcitic siliciclastic 0.0273 -2.593×10-7 4.591 0.002705 -3.68×10-8 0.9408 siliceous dolostone 0.04368 -0.8411×10-7 10.40 0.006605 -1.834×10-7 0.9839 siliceous limestone 0.04417 -1.003×10-6 14.00 0.005247 -1.539×10-7 0.9658 pure limestone + SiO2(aq) 0.06956 -1.796×10-7 11.22 0.01071 -3.514×10-7 0.9654 pure dolostone + SiO2 (aq) 0.05029 -1.179×10-6 13.04 0.006222 -1.935×10-7 0.9841

Article Title: Distorted time window for sensorimotor integration and preserved time window for sense of agency in patients with post-stroke limb apraxia
Article Snippet: Fitting was performed using a nonlinear least squares method (a trust-region algorithm), provided by the Curve Fitting toolbox in MATLAB R2016a (MathWorks, Natick, MA, United States), to estimate a (signifying the steepness of the logistic curve) and tDDT.

Article Title: Identifying nitrate pollution of groundwater based on aquifer vulnerability and surface nitrogen loads in the Northern Foot of the Qinling Mountains, China.
Article Snippet: • The modified DRASTIC model provides a more objective assessment of aquifer

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( a ) Overview of the Spectral Parameterization Resolved in Time (SPRiNT) approach: At each time bin along a neurophysiological time series (black trace) n overlapping time windows are Fourier-transformed to yield an estimate of spectral contents, which is subsequently parameterized using specparam . The procedure is replicated across time over sliding, overlapping windows to generate a parameterized spectrogram of neural activity. ( b ) Simulation challenge I: We simulated 10,000 time series composed of the same time-varying spectral (aperiodic and periodic) features, with different realizations of additive noise. ( c ) Simulation challenge II: We simulated another 10,000 time series, each composed of different time-varying spectral (aperiodic and periodic) ground-truth features with additive noise. All simulated time series were used to evaluate the respective performances of SPRiNT and the wavelet- specparam alternative.

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: ( a ) Overview of the Spectral Parameterization Resolved in Time (SPRiNT) approach: At each time bin along a neurophysiological time series (black trace) n overlapping time windows are Fourier-transformed to yield an estimate of spectral contents, which is subsequently parameterized using specparam . The procedure is replicated across time over sliding, overlapping windows to generate a parameterized spectrogram of neural activity. ( b ) Simulation challenge I: We simulated 10,000 time series composed of the same time-varying spectral (aperiodic and periodic) features, with different realizations of additive noise. ( c ) Simulation challenge II: We simulated another 10,000 time series, each composed of different time-varying spectral (aperiodic and periodic) ground-truth features with additive noise. All simulated time series were used to evaluate the respective performances of SPRiNT and the wavelet- specparam alternative.

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques: Transformation Assay, Activity Assay

( a ) Ground-truth spectrogram (left) and averaged modelled spectrograms produced by the wavelet- specparam approach (middle) and SPRiNT (right; n=10,000). ( b ) Aperiodic parameter estimates (lines: median; shaded regions: first and third quartiles, n=10,000) across time from wavelet- specparam (left) and SPRiNT (right; black: ground truth; blue: exponent; yellow: offset). ( c ) Absolute error (and detection performance) of alpha and beta-band rhythmic components for wavelet- specparam (left) and SPRiNT (right). Violin plots represent the sample distributions (n=10,000; blue: alpha peak; yellow: beta peak; white circle: median, grey box: first and third quartiles; whiskers: range).

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: ( a ) Ground-truth spectrogram (left) and averaged modelled spectrograms produced by the wavelet- specparam approach (middle) and SPRiNT (right; n=10,000). ( b ) Aperiodic parameter estimates (lines: median; shaded regions: first and third quartiles, n=10,000) across time from wavelet- specparam (left) and SPRiNT (right; black: ground truth; blue: exponent; yellow: offset). ( c ) Absolute error (and detection performance) of alpha and beta-band rhythmic components for wavelet- specparam (left) and SPRiNT (right). Violin plots represent the sample distributions (n=10,000; blue: alpha peak; yellow: beta peak; white circle: median, grey box: first and third quartiles; whiskers: range).

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques: Produced

( a ) Results from the temporally smoothed wavelet -specparam approach for the alpha (top) and beta (bottom) rhythmic components for each estimated parameter (from left to right: centre frequency, spectral peak amplitude, and SD). Grey dashed line: ground truth; coloured line: median; shaded region: first and third quartiles. Bar plots in left panels: probability of detecting an oscillatory peak within respective frequency ranges at each time bin. ( b ) Same display for the results obtained with Spectral Parameterization Resolved in Time (SPRiNT). All with n=10,000 simulations.

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: ( a ) Results from the temporally smoothed wavelet -specparam approach for the alpha (top) and beta (bottom) rhythmic components for each estimated parameter (from left to right: centre frequency, spectral peak amplitude, and SD). Grey dashed line: ground truth; coloured line: median; shaded region: first and third quartiles. Bar plots in left panels: probability of detecting an oscillatory peak within respective frequency ranges at each time bin. ( b ) Same display for the results obtained with Spectral Parameterization Resolved in Time (SPRiNT). All with n=10,000 simulations.

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques:

( a ) Aperiodic parameter estimates (lines: median; shaded regions: first and third quartiles, n=1000) across time from wavelet -specparam with full width at half maximum (FWHM) = 2 s (left) and wavelet- specparam with FWHM = 4 s (right; black dash: ground truth; blue: exponent; yellow: offset). ( b ) Absolute error (and detection performance) of alpha and beta-band rhythmic components for wavelet -specparam with FWHM = 2 s (left) and wavelet- specparam with FWHM = 4 s (right). Violin plots represent the sample distributions (n=1000; blue: alpha peak; yellow: beta peak; white circle: median, grey box: first and third quartiles; whiskers: range).

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: ( a ) Aperiodic parameter estimates (lines: median; shaded regions: first and third quartiles, n=1000) across time from wavelet -specparam with full width at half maximum (FWHM) = 2 s (left) and wavelet- specparam with FWHM = 4 s (right; black dash: ground truth; blue: exponent; yellow: offset). ( b ) Absolute error (and detection performance) of alpha and beta-band rhythmic components for wavelet -specparam with FWHM = 2 s (left) and wavelet- specparam with FWHM = 4 s (right). Violin plots represent the sample distributions (n=1000; blue: alpha peak; yellow: beta peak; white circle: median, grey box: first and third quartiles; whiskers: range).

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques:

( a ) Aperiodic parameter estimates (lines: median; shaded regions: first and third quartiles, n=1000) across time from unsmoothed wavelet- specparam (left) and SPRiNT without outlier peak removal (right; black dash: ground truth; blue: exponent; yellow: offset). ( b ) Absolute error (and detection performance) of alpha and beta-band periodic components for unsmoothed wavelet- specparam (left) and SPRiNT without outlier peak removal (right). Violin plots represent the sample distributions (n=1000; blue: alpha peak; yellow: beta peak; white circle: median, grey box: first and third quartiles; whiskers: range).

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: ( a ) Aperiodic parameter estimates (lines: median; shaded regions: first and third quartiles, n=1000) across time from unsmoothed wavelet- specparam (left) and SPRiNT without outlier peak removal (right; black dash: ground truth; blue: exponent; yellow: offset). ( b ) Absolute error (and detection performance) of alpha and beta-band periodic components for unsmoothed wavelet- specparam (left) and SPRiNT without outlier peak removal (right). Violin plots represent the sample distributions (n=1000; blue: alpha peak; yellow: beta peak; white circle: median, grey box: first and third quartiles; whiskers: range).

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques:

( a ) Mean periodogram and specparam models for eyes-closed (blue) and eyes-open (yellow) resting-state EEG activity (from electrode Oz; n=178). ( b ) Logistic regressions showed that specparam -derived eyes-closed alpha-peak amplitude was predictive of age group, but mean eyes-closed alpha-peak amplitude derived from SPRiNT was not. ( c ) Example of intrinsic dynamics in alpha activity during the eyes-closed period leading to divergent SPRiNT and specparam models (participant sub-016). In a subset of participants (<10%), we observed strong intermittence of the presence of an alpha peak. Since an alpha peak was not consistently present in the eyes-closed condition, and specparam -derived alpha-peak amplitude (0.77 a.u.; light blue) is lower than SPRiNT-derived mean alpha-peak amplitude (1.06 a.u.; dark blue), as the latter only includes time samples featuring a detected alpha peak. ( d ) Logistic regression showed that temporal variability in eyes-open alpha centre frequency predicts age group. Left: mean SPRiNT spectrogram (n=178) and sample distribution of eyes-open alpha centre frequency (participant sub-067). Right: variability (SD) in eyes-open alpha centre frequency separated by age group. Note: no alpha peaks were detected in the eyes-open period for one participant (boxplot line: median; boxplot limits: first and third quartiles; whiskers: range). Sample sizes: younger adults (age: 20–40 years): 121; older adults (age: 55–80 years): 56. Figure 4—source data 1. Spectral parameters and age group by participant.

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: ( a ) Mean periodogram and specparam models for eyes-closed (blue) and eyes-open (yellow) resting-state EEG activity (from electrode Oz; n=178). ( b ) Logistic regressions showed that specparam -derived eyes-closed alpha-peak amplitude was predictive of age group, but mean eyes-closed alpha-peak amplitude derived from SPRiNT was not. ( c ) Example of intrinsic dynamics in alpha activity during the eyes-closed period leading to divergent SPRiNT and specparam models (participant sub-016). In a subset of participants (<10%), we observed strong intermittence of the presence of an alpha peak. Since an alpha peak was not consistently present in the eyes-closed condition, and specparam -derived alpha-peak amplitude (0.77 a.u.; light blue) is lower than SPRiNT-derived mean alpha-peak amplitude (1.06 a.u.; dark blue), as the latter only includes time samples featuring a detected alpha peak. ( d ) Logistic regression showed that temporal variability in eyes-open alpha centre frequency predicts age group. Left: mean SPRiNT spectrogram (n=178) and sample distribution of eyes-open alpha centre frequency (participant sub-067). Right: variability (SD) in eyes-open alpha centre frequency separated by age group. Note: no alpha peaks were detected in the eyes-open period for one participant (boxplot line: median; boxplot limits: first and third quartiles; whiskers: range). Sample sizes: younger adults (age: 20–40 years): 121; older adults (age: 55–80 years): 56. Figure 4—source data 1. Spectral parameters and age group by participant.

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques: Activity Assay, Derivative Assay

Logistic regression model of specparam parameters for predicting condition (eyes-closed vs eyes-open).

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: Logistic regression model of specparam parameters for predicting condition (eyes-closed vs eyes-open).

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques:

Eyes-open logistic regression model parameters for predicting age group, specparam .

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: Eyes-open logistic regression model parameters for predicting age group, specparam .

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques:

Eyes-closed logistic regression model parameters for predicting age group, specparam .

Journal: eLife

Article Title: Time-resolved parameterization of aperiodic and periodic brain activity

doi: 10.7554/eLife.77348

Figure Lengend Snippet: Eyes-closed logistic regression model parameters for predicting age group, specparam .

Article Snippet: We also parameterized Morlet wavelet spectrograms of each simulated time series using specparam ( ; MATLAB version).

Techniques:

Brief overview of key settings for the specparam algorithm.

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:

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.

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:

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.

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:

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.)

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

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spectral parameterization (specparam) toolbox (MathWorks Inc)

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