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Correlation Networks of Spinal Motor Neurons that Innervate Lower Limb Muscles during a Multi-Joint Isometric Task
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dataset
modified on 2022-07-07, 12:09 This repository contains all the raw HD-EMG data, the manually edited motor unit spike trains and the data selected to build networks of common inputs to motor neurons.
Contact:
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FILE NAME: Raw_data.zip
This compressed file contains the raw data together with the motor unit spike trains manually edited.
The HDsEMG signals were decomposed into motor unit spike trains using convolutive blind-source separation as described in the manuscript (custom made matlab script). After the automatic identification of the motor units, the spike trains were visually checked for false positives and false negatives using the DEMUSE tool (The University of Maribor, Slovenia).
Filename: [Participant number]_135_175.otb+_[muscle]_Niter150_FastICA_edited_final.mat
VARIABLES (only the variables used in the manuscript are described below) :
- DecompRuns: number of decomposition runs - Note that this information is incorrect on these files
- discardChannelsVec: discarded channels for the 13*5 EMG grid (ELSCH064NM2, SpesMedica, Battipaglia). "1" means that the channnel has been discarded due to noise/artifacts. Note that one electrode was absent on a corner [1,1].
- fsamp: sampling rate
- IED: inter-electrode distance
- IPTs: Innervation Pulse Train (IPT, i.e. train of motor unit discharge times as estimated by the decomposition technique)
- MUPulses (1*number of motor units array). For each identified units, there is a vector of motor units discharge times (in datapoint).
- PNR: Pulse-to-noise ratio for each identified MU - Of note, these values are calculated for the whole signal and therefore they do not correspond to the PNR of the processed 10-s windows.
- ref_signal: force signal (only z component)
- SIG (13*5 cell array): raw EMG signal organised on a 13*5 cell array. Each cell corresponds to an EMG channel.
Contact:
Francois Hug (francois.hug@univ-cotedazur.fr)
Simon Avrillon (savrillon@sralab.org)
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FILE NAME: Raw_data.zip
This compressed file contains the raw data together with the motor unit spike trains manually edited.
The HDsEMG signals were decomposed into motor unit spike trains using convolutive blind-source separation as described in the manuscript (custom made matlab script). After the automatic identification of the motor units, the spike trains were visually checked for false positives and false negatives using the DEMUSE tool (The University of Maribor, Slovenia).
Filename: [Participant number]_135_175.otb+_[muscle]_Niter150_FastICA_edited_final.mat
Participant: from #1 to #10.
Muscle: GM (gastrocnemius medialis), GL (gastrocnemius lateralis), VL (vastus lateralis), VM (vastus medialis), BF (biceps femoris), ST (semitendinosus)
These files can be opened with Matlab (Mathworks) or the DEMUSE tool.
- DecompRuns: number of decomposition runs - Note that this information is incorrect on these files
- discardChannelsVec: discarded channels for the 13*5 EMG grid (ELSCH064NM2, SpesMedica, Battipaglia). "1" means that the channnel has been discarded due to noise/artifacts. Note that one electrode was absent on a corner [1,1].
- fsamp: sampling rate
- IED: inter-electrode distance
- IPTs: Innervation Pulse Train (IPT, i.e. train of motor unit discharge times as estimated by the decomposition technique)
- MUPulses (1*number of motor units array). For each identified units, there is a vector of motor units discharge times (in datapoint).
- PNR: Pulse-to-noise ratio for each identified MU - Of note, these values are calculated for the whole signal and therefore they do not correspond to the PNR of the processed 10-s windows.
- ref_signal: force signal (only z component)
- SIG (13*5 cell array): raw EMG signal organised on a 13*5 cell array. Each cell corresponds to an EMG channel.
- SIGlength: duration of the signal in seconds
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FILE NAME: Processed_data.zip
This compressed file contains the processed data for the two different analyses, i.e. repeatability and construction of the networks.
FILE NAME: Processed_data.zip
This compressed file contains the processed data for the two different analyses, i.e. repeatability and construction of the networks.
Data for the reliability between matrices of correlation:
Name: ’S ‘number of participant’ _repro.mat
Structure:
Name: ’S ‘number of participant’ _repro.mat
Structure:
data.muscle: name of the motor unit with the name of the muscle and the number of the motor unit. e.g., BF1.
data.firings: matrix of motor unit’s discharge times. This is a binary file where each discharge time = 1.
data.time: time for the motor unit’s discharge times.
data.Matcorrel_Win1: matrix of correlations for each pair of motor units over the first window.
data.Matcorrel_Win2: matrix of correlations between each pair of motors unit over the second window.
Data for the network analysis:
Name: ’S ‘number of participant’ _data.mat
Structure:
data.Muscle: name of the motor unit with the name of the muscle and the number of the motor unit. e.g., BF1.
data.firings: matrix of motor unit’s discharge times. This is a binary file where each discharge time = 1.
data.time: time for the motor unit’s discharge times.
data.matcorrel: matrix of correlations for each pair of motor units.
data.fsamp: sample frequency, I.e., 2048 Hz.
data.firings: matrix of motor unit’s discharge times. This is a binary file where each discharge time = 1.
data.time: time for the motor unit’s discharge times.
data.Matcorrel_Win1: matrix of correlations for each pair of motor units over the first window.
data.Matcorrel_Win2: matrix of correlations between each pair of motors unit over the second window.
Data for the network analysis:
Name: ’S ‘number of participant’ _data.mat
Structure:
data.Muscle: name of the motor unit with the name of the muscle and the number of the motor unit. e.g., BF1.
data.firings: matrix of motor unit’s discharge times. This is a binary file where each discharge time = 1.
data.time: time for the motor unit’s discharge times.
data.matcorrel: matrix of correlations for each pair of motor units.
data.fsamp: sample frequency, I.e., 2048 Hz.
