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matlab real-time implementation  (MathWorks Inc)


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    MathWorks Inc matlab real-time implementation
    ( A ) Example 2 s of simulated observed data (thick curves) and the true phase (thin blue curves) for each scenario. ( B ) For each scenario, example spectra of (B.i) the signal, and (B.ii) the observation (i.e., signal plus noise). Spectra were estimated for 10 s segments using the function ‘pmtm’ in <t>MATLAB,</t> to compute a multitaper estimate with frequency resolution 1 Hz and nine tapers. ( C ) The phase error for each estimation method (see legend) and simulation scenario. In each box plot, the central mark indicates the median; the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively; the whiskers indicate the most extreme data points not considered outliers. Figure 2—source data 1. Circular standard deviation for all methods.
    Matlab Real Time Implementation, 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/matlab real-time implementation/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab real-time implementation - by Bioz Stars, 2026-03
    90/100 stars

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    1) Product Images from "A state space modeling approach to real-time phase estimation"

    Article Title: A state space modeling approach to real-time phase estimation

    Journal: eLife

    doi: 10.7554/eLife.68803

    ( A ) Example 2 s of simulated observed data (thick curves) and the true phase (thin blue curves) for each scenario. ( B ) For each scenario, example spectra of (B.i) the signal, and (B.ii) the observation (i.e., signal plus noise). Spectra were estimated for 10 s segments using the function ‘pmtm’ in MATLAB, to compute a multitaper estimate with frequency resolution 1 Hz and nine tapers. ( C ) The phase error for each estimation method (see legend) and simulation scenario. In each box plot, the central mark indicates the median; the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively; the whiskers indicate the most extreme data points not considered outliers. Figure 2—source data 1. Circular standard deviation for all methods.
    Figure Legend Snippet: ( A ) Example 2 s of simulated observed data (thick curves) and the true phase (thin blue curves) for each scenario. ( B ) For each scenario, example spectra of (B.i) the signal, and (B.ii) the observation (i.e., signal plus noise). Spectra were estimated for 10 s segments using the function ‘pmtm’ in MATLAB, to compute a multitaper estimate with frequency resolution 1 Hz and nine tapers. ( C ) The phase error for each estimation method (see legend) and simulation scenario. In each box plot, the central mark indicates the median; the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively; the whiskers indicate the most extreme data points not considered outliers. Figure 2—source data 1. Circular standard deviation for all methods.

    Techniques Used: Standard Deviation

    ( A ) Open Ephys GUI for using SSPE. The user specifies the number of frequencies to track, the center frequencies to track, the frequencies of interest for phase calculation and output (FOI), variance for the FOI, and the observation error. Observation error determines the effective bandwidth for each frequency. ( B ) Histogram of the circular standard deviation between MATLAB (offline) and TORTE (real-time) implementations of the SSPE. Small variation results from causal low pass filtering in TORTE and acausal filtering in the offline phase estimates. ( C ) Time to evaluate phase versus buffer size. Longer buffer sizes from TORTE require longer calculation time for application of SSPE. However, the calculation time is approximately two orders of magnitude smaller than the buffer size.
    Figure Legend Snippet: ( A ) Open Ephys GUI for using SSPE. The user specifies the number of frequencies to track, the center frequencies to track, the frequencies of interest for phase calculation and output (FOI), variance for the FOI, and the observation error. Observation error determines the effective bandwidth for each frequency. ( B ) Histogram of the circular standard deviation between MATLAB (offline) and TORTE (real-time) implementations of the SSPE. Small variation results from causal low pass filtering in TORTE and acausal filtering in the offline phase estimates. ( C ) Time to evaluate phase versus buffer size. Longer buffer sizes from TORTE require longer calculation time for application of SSPE. However, the calculation time is approximately two orders of magnitude smaller than the buffer size.

    Techniques Used: Standard Deviation



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    Image Search Results


    ( A ) Example 2 s of simulated observed data (thick curves) and the true phase (thin blue curves) for each scenario. ( B ) For each scenario, example spectra of (B.i) the signal, and (B.ii) the observation (i.e., signal plus noise). Spectra were estimated for 10 s segments using the function ‘pmtm’ in MATLAB, to compute a multitaper estimate with frequency resolution 1 Hz and nine tapers. ( C ) The phase error for each estimation method (see legend) and simulation scenario. In each box plot, the central mark indicates the median; the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively; the whiskers indicate the most extreme data points not considered outliers. Figure 2—source data 1. Circular standard deviation for all methods.

    Journal: eLife

    Article Title: A state space modeling approach to real-time phase estimation

    doi: 10.7554/eLife.68803

    Figure Lengend Snippet: ( A ) Example 2 s of simulated observed data (thick curves) and the true phase (thin blue curves) for each scenario. ( B ) For each scenario, example spectra of (B.i) the signal, and (B.ii) the observation (i.e., signal plus noise). Spectra were estimated for 10 s segments using the function ‘pmtm’ in MATLAB, to compute a multitaper estimate with frequency resolution 1 Hz and nine tapers. ( C ) The phase error for each estimation method (see legend) and simulation scenario. In each box plot, the central mark indicates the median; the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively; the whiskers indicate the most extreme data points not considered outliers. Figure 2—source data 1. Circular standard deviation for all methods.

    Article Snippet: The computed parameters were nearly identical between the two implementations and the time to estimate the parameters (which, as above, would need to be done only once per experiment) ranged from 2 to 100 s. The phase estimates of the two implementations have a mean circular difference of 2.06 degrees ( ) with a circular standard deviation of 0.65 degree; this variation in phase estimates likely arises from differences in filtering in the TORTE real-time (causal filtering on individual buffers) implementation and the offline MATLAB real-time (acausal filtering across all data) implementation.

    Techniques: Standard Deviation

    ( A ) Open Ephys GUI for using SSPE. The user specifies the number of frequencies to track, the center frequencies to track, the frequencies of interest for phase calculation and output (FOI), variance for the FOI, and the observation error. Observation error determines the effective bandwidth for each frequency. ( B ) Histogram of the circular standard deviation between MATLAB (offline) and TORTE (real-time) implementations of the SSPE. Small variation results from causal low pass filtering in TORTE and acausal filtering in the offline phase estimates. ( C ) Time to evaluate phase versus buffer size. Longer buffer sizes from TORTE require longer calculation time for application of SSPE. However, the calculation time is approximately two orders of magnitude smaller than the buffer size.

    Journal: eLife

    Article Title: A state space modeling approach to real-time phase estimation

    doi: 10.7554/eLife.68803

    Figure Lengend Snippet: ( A ) Open Ephys GUI for using SSPE. The user specifies the number of frequencies to track, the center frequencies to track, the frequencies of interest for phase calculation and output (FOI), variance for the FOI, and the observation error. Observation error determines the effective bandwidth for each frequency. ( B ) Histogram of the circular standard deviation between MATLAB (offline) and TORTE (real-time) implementations of the SSPE. Small variation results from causal low pass filtering in TORTE and acausal filtering in the offline phase estimates. ( C ) Time to evaluate phase versus buffer size. Longer buffer sizes from TORTE require longer calculation time for application of SSPE. However, the calculation time is approximately two orders of magnitude smaller than the buffer size.

    Article Snippet: The computed parameters were nearly identical between the two implementations and the time to estimate the parameters (which, as above, would need to be done only once per experiment) ranged from 2 to 100 s. The phase estimates of the two implementations have a mean circular difference of 2.06 degrees ( ) with a circular standard deviation of 0.65 degree; this variation in phase estimates likely arises from differences in filtering in the TORTE real-time (causal filtering on individual buffers) implementation and the offline MATLAB real-time (acausal filtering across all data) implementation.

    Techniques: Standard Deviation