Keywords: surface high density electromyogram (HDEMG), motor unit, simulation, biceps brachii, elbox flexion, dataset

SHARING/ACCESS INFORMATION
This dataset is available under the Creative Commons Public Domain Dedication (CC0) license.

Publicly accessible location of the data: https://dk.um.si/IzpisGradiva.php?lang=eng&id=88843

PID: 20.500.12556/DKUM-88843

Recommended citation for this dataset:
Holobar, Aleš, Farina, Dario. “Simulated HDEMG data of biceps brachii muscle during isometric elbow flexion”. System Software Laboratory, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia. Digital library of University of Maribor. https://dk.um.si/IzpisGradiva.php?lang=eng&id=88843

DATA & FILE OVERVIEW
This dataset contains a collection of 150 simulated surface HDEMG recordings of the biceps brachii muscle during the isometric elbow flexion. Data was simulated using the cylindrical volume conductor model [1] and the motor unit recruitment and firing modulation model proposed in [2]. Each recording is 30 seconds in length with 90 HDEMG channels and sampled at 2048 Hz and is stored as a 2D matrix of raw EMG values in Matlab's .MAT format.

The dataset is 6 GB in size.

File list:

• README.txt: this text file containing description of the dataset
• metadata.xml: Dublin Core metadata in XML format
• folders "Lib1" to "Lib10" contain data for 10 different (simulated) subjects
• each folder contains 15 files with simulated HDEMG recordings of the biceps brachii muscle:
•      - for 3 different levels of added noise (as signal-to-ratio - SNR in dB): Inf, 20, 15
•      - for 5 different excitation levels (as percentage of maximum voluntary contraction - MVC): 10, 30, 50, 70, 90
• filenames are in format:
•      - SyntheticHDEMG_BicepsBrachii_Lib<lib_no>_ConstantExcitation<mvc_level>MVC_SigLen30_SNR<snr_level>.mat, where LIB_NO is the subject number, MVC_LEVEL is the excitation level as percentage of MVC and SNR_LEVEL is the signal-to-noise ratio in dB
• MAT_file_content.png: screenshot of the Matlab workspace with all the variables loaded from the MAT file

All files with extension .MAT are in Matlab format, which is a proprietary format but its specifications are open and there are open source routines available for reading and writing:

• The GNU Octave software, which is an open source alternative to Matlab, can read and write .MAT files.
• The Python library Scipy can load MAT files.
• The Matio project on SourceForge is a C library for reading and writing Matlab MAT files.

METHODOLOGICAL INFORMATION
Description of methods used for collection/generation of data:
The synthetic multichannel sEMG data were generated by a multilayer cylindrical volume conductor model [1] for the biceps brachii muscle.

Model parameters:

• Motor unit conduction velocity: 4 m/s
• Tissue conductivities (in S/m):
•      - Bone: 0.02
•      - Muscle (radial and transverse): 0.1
•      - Muscle (longitudinal): 0.5
•      - Subcutaneous tissue: 0.05
•      - Skin: 0.1
• Muscle properties:
•      - Number of motor units: 500
•      - Number of muscle fibers: 165656
•      - Number of fibers in a motor unit (range): 25 - 1500
•      - Muscle cross-sectional area: 1413 square mm
•      - Average fiber length: 130 mm
•      - Skin thickness: 1 mm
•      - Subcutaneous tissue thickness: 4 mm
•      - Bone (radius): 20 mm
•      - Tendon ending spread: 5 mm
• Electrodes:
•      - Grid: 10 rows x 9 columns
•      - Circular (diameter): 0.5 mm
•      - Interelectrode distance: 5 mm
• Excitation level (% maximum):
•      - Constant force: 10, 30, 50, 70, 90 % of maximum voluntary contraction (MVC)
• Innervation zone in the middle
• Sample rate: 4096 Hz
• Total length: 30 s
• SNR: infinite (no noise added), 20 dB, 15 dB
• Monopolar recording mode

Motor unit (MU) firing pattern model: The MU firing pattern was computed by the model proposed in [2] with the parameters adapted to the biceps brachii muscle. MU recruitment thresholds followed an exponential distribution with many low-threshold MUs and progressively fewer high-threshold MUs [2]. The last MU was recruited at 80% of maximal excitation level. MUs linearly increased the firing rates from 8 pulses per second (pps) at recruitment to 35 pps at 100 % of maximal excitation. Interspike interval of each MU followed Gaussian distribution with coefficient of variation equal to 20 %.

Methods for processing the data:
The raw multichannel sEMG signals generated by the simulator were downsampled to 2048 Hz, which is closer to sampling rate typically used in experimental recordings. No other signal processing was performed.

Quality-assurance procedures performed on the data: Data from all files was plotted and visually inspected for any errors or anomalies, but none were rejected.

DATA-SPECIFIC INFORMATION FOR ALL .MAT FILES:
Number of variables: 9

Variable list:

• fsamp: integer number. Data sampling frequency in Hz.
• SigLength: double number. Total length of the signals in seconds.
• HDEMG: structure array. Dimensions are the same as the simulated EMG electrode grid: 10 rows x 9 columns. Each cell contains a 1 x N vector of samples (double values) representing EMG voltage. N = total number of samples = fsamp * SigLength.
• sFirings: structure array. Contains M cells corresponding to M simulated motor units, each cell contains a 1 x L vector of motor unit firing instants (integer values indicating sample indices). M = number of simulated motor units, L = number of motor unit firings.
• library: integer number. Numerical ID label of the simulated subject.
• excitationLevel: double array, 1 x N in size. Contains the excitation level for each data sample.
• SNR: double number. Signal-to-noise ratio in dB.
• recordingMode: string. Denotes the simulated voltage measurement technique, "MONOPOLAR" for this dataset.
• InterElectrodeDistance: double number. Interelectrode distance in mm.

REFERENCES:
[1] Farina, Dario, Luca Mesin, Simone Martina, and Roberto Merletti. "A surface EMG generation model with multilayer cylindrical description of the volume conductor." IEEE Transactions on Biomedical Engineering 51, no. 3 (2004): 415-426. doi: 10.1109/TBME.2003.820998.

[2] Fuglevand, Andrew J., David A. Winter, and Aftab E. Patla. "Models of recruitment and rate coding organization in motor-unit pools." Journal of neurophysiology 70, no. 6 (1993): 2470-2488.