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xcor matlab function  (MathWorks Inc)


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    MathWorks Inc xcor matlab function
    Xcor Matlab Function, 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/xcor+function+of/pm38651438-129-41-42
    Average 90 stars, based on 1 article reviews
    xcor matlab function - by Bioz Stars, 2026-11
    90/100 stars

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    Article Title: Method to Quickly Map Multifocal Pupillary Response Fields (mPRF) Using Frequency Tagging.
    Article Snippet: Vision 2024, 8, 17 12 of 26 Vision 2024, 8, x FOR PEER REVIEW 13 of 29 Figure 5. (A): Example of an individual stimulus/pupillary signal cross-correlation using the Matlab xcor function. (B): Histogram of phase lags for all participants.

    Article Title: Method to Quickly Map Multifocal Pupillary Response Fields (mPRF) Using Frequency Tagging.
    Article Snippet: Figure 5. (A): Example of an individual stimulus/pupillary signal cross-correlation using the Matlab xcor function. (B): Histogram of phase lags for all participants.

    Muscles:

    Article Title: Method to Quickly Map Multifocal Pupillary Response Fields (mPRF) Using Frequency Tagging.
    Article Snippet: .. In addition, the cross-correlation between the average time-varying luminance modulation of the stimulus and the pupillary oscillations (after detrending the pupillary signal to suppress low frequency and linear drift components) was computed to derive the lag between both signals using the xcor Matlab function, thus providing an overall estimate of the time needed for retinal processing, propagation time through the optic nerve and relay nuclei, as well as the mechanical constriction and dilation time constants of the iris sphincter and dilator muscles. ..



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    ( A ): Example of an individual stimulus/pupillary signal cross-correlation using <t>the</t> <t>Matlab</t> <t>xcor</t> function. ( B ): Histogram of phase lags for all participants. Bottom left: right eye; bottom middle: left eye. ( C ): Correlation between cross-correlation lags of the right and left eyes of all participants. Red lines show the linear regression (r = 083, p < 0.0001) together with 95% confidence intervals. Note that because pupillary signals are down-sampled to 60 Hz, the time resolution of lags is only 16.666 ms such that phase lags from different participants overlap. The high correlation shown here indicate that similar lags are observed for the right and left eyes of each participant.
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    MathWorks Inc matlab xcor function
    ( A ): Example of an individual stimulus/pupillary signal cross-correlation using <t>the</t> <t>Matlab</t> <t>xcor</t> function. ( B ): Histogram of phase lags for all participants. Bottom left: right eye; bottom middle: left eye. ( C ): Correlation between cross-correlation lags of the right and left eyes of all participants. Red lines show the linear regression (r = 083, p < 0.0001) together with 95% confidence intervals. Note that because pupillary signals are down-sampled to 60 Hz, the time resolution of lags is only 16.666 ms such that phase lags from different participants overlap. The high correlation shown here indicate that similar lags are observed for the right and left eyes of each participant.
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    ( A ): Example of an individual stimulus/pupillary signal cross-correlation using the Matlab xcor function. ( B ): Histogram of phase lags for all participants. Bottom left: right eye; bottom middle: left eye. ( C ): Correlation between cross-correlation lags of the right and left eyes of all participants. Red lines show the linear regression (r = 083, p < 0.0001) together with 95% confidence intervals. Note that because pupillary signals are down-sampled to 60 Hz, the time resolution of lags is only 16.666 ms such that phase lags from different participants overlap. The high correlation shown here indicate that similar lags are observed for the right and left eyes of each participant.

    Journal: Vision

    Article Title: Method to Quickly Map Multifocal Pupillary Response Fields (mPRF) Using Frequency Tagging

    doi: 10.3390/vision8020017

    Figure Lengend Snippet: ( A ): Example of an individual stimulus/pupillary signal cross-correlation using the Matlab xcor function. ( B ): Histogram of phase lags for all participants. Bottom left: right eye; bottom middle: left eye. ( C ): Correlation between cross-correlation lags of the right and left eyes of all participants. Red lines show the linear regression (r = 083, p < 0.0001) together with 95% confidence intervals. Note that because pupillary signals are down-sampled to 60 Hz, the time resolution of lags is only 16.666 ms such that phase lags from different participants overlap. The high correlation shown here indicate that similar lags are observed for the right and left eyes of each participant.

    Article Snippet: The temporal lags (computed with the xcor function of Matlab for each run) are around 500 ms (range 420–540 ms, ) and are comparable to the maximum constriction latencies reported for the PLR, ranging between 500 and 600 ms [ , , ].

    Techniques: