Individual differences in the neural strategies to control the lateral and medial head of the quadriceps during a mechanically constrained task

This repository contains all the raw HD-EMG data, the extracted and manually edited motor units, and the binary spike trains used to calculate the within- and between-muscle coherence.<br><br>Contact: <br>Francois Hug ([e-mail])Simon Avrillon ([e-mail])<br>=================================<br><br>FILE NAME: Decomposition_results.zip <br>This compressed file contains raw data for each muscle. Motor units were identified and manually edited (FH and SA) using the DEMUSE tool (v4.9; The University of Maribor, Slovenia)<br> [participant_number]_[muscle]_edited_v1.mat These files can be opened with Matlab 2015b (Mathworks) or the DEMUSE tool. VARIABLES (only the variables used in the manuscript are described below) :<br> - DecompRuns: number of decomposition runs (50 for most of the 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 Rrain (IPT, i.e. train of motor unit discharge times as estimated by the gCKC 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 - ref_signal: force signal (not calibrated) - 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<br>=================================<br><br>FILE NAME: Data.mat<br>The file data.mat contains a structure of cells with torque data and the binary spike trains from the 22 participants. They are the data used to calculate the within- and between-muscle coherence.<br><br>In the first level of the structure, there are 22 cells where each cell is a participant. e.g., Data{4,1} is the data for participant #4.<br>In the second level of the structure, there is a matrix of cells with 2 lines and 4 columns.<br>Each line is a muscle with line 1 = VL and line 2 = VM.<br><br>Columns 1, 2 and 3 are the data for contractions 1, 2, and 3. Column 4 is the concatenate data for coherence analysis.<br><br>e.g., Data{4,1}{1,2} is the data for the VL during the second contraction from participant #4.<br><br>In the third level, you can find a matrix with torque data and motor unit binary spike trains<br><br>Line 1 = Time<br>Line 2 = Torque<br>Line 3:end = binary spike trains (one motor unit per line)<br><br>e.g., Data{4,1}{1,2}(2,:) is the torque during the second contraction from participant #4.<br><br>e.g., Data{4,1}{1,2}(3,:) is the binary spike train of motor unit #1 of the VL during the second contraction from participant #4.<br>