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matlab-functions islocalmax  (MathWorks Inc)


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    MathWorks Inc matlab-functions islocalmax
    Matlab Functions Islocalmax, 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/matlab+function+islocalmax/pmc10292016-134-8-8
    Average 90 stars, based on 1 article reviews
    matlab-functions islocalmax - by Bioz Stars, 2026-09
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    Article Title: Influence of Messa di Voce speed on vocal stability of professionally trained singers.
    Article Snippet: To analyze the modulation of f o over time and reconstruct the resulting minima and maxima from GAW cycles, extrema recognition was applied using Matlab’s islocalmin and islocalmax functions [39].

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    matlab function 'islocalmax  (MathWorks Inc)
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    MathWorks Inc matlab function 'islocalmax
    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    islocalmax function of matlab 2022a  (MathWorks Inc)
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    islocalmax function matlab 2022a  (MathWorks Inc)
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    matlab function islocalmax  (MathWorks Inc)
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    matlab-functions islocalmax  (MathWorks Inc)
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    matlab functions islocalmax  (MathWorks Inc)
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
    Matlab Functions Islocalmax, 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
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    matlab islocalmax function  (MathWorks Inc)
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    islocalmax (signal, minimal distance, and minimal peak prominence) matlab function  (MathWorks Inc)
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
    Islocalmax (Signal, Minimal Distance, And Minimal Peak Prominence) 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
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    islocalmax (signal, minimal distance) matlab function  (MathWorks Inc)
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    MathWorks Inc islocalmax (signal, minimal distance) matlab function
    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
    Islocalmax (Signal, Minimal Distance) 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
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function ‘islocalmax’. Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.

    Journal: eLife

    Article Title: I-Spin live, an open-source software based on blind-source separation for real-time decoding of motor unit activity in humans

    doi: 10.7554/eLife.88670

    Figure Lengend Snippet: ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function ‘islocalmax’. Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.

    Article Snippet: Local peaks were identified for each motor unit using the MATLAB function ‘islocalmax’ with a minimal separation of 25 ms between peaks to limit the number of false positives ( ).

    Techniques: Software, Plasmid Preparation, Activity Assay