High-density surface EMG + EEG recordings of isometric ankle dorsiflexion, imaginary movement and GO/NO-GO tasks
Keywords: surface high density electromyogram (HDEMG), electroencephalogram (EEG), dataset, tibialis anterior, isometric ankle dorsiflexion, GO/NO-GO task, imaginary movement, corticomuscular coherence, motor unit filter, HybridNeuro
SHARING/ACCESS INFORMATION
License: This dataset is available under the Creative Commons
Attribution (CC BY) 4.0 license.
Publicly accessible location of the data:
https://dk.um.si/IzpisGradiva.php?lang=eng&id=96398
PID: 20.500.12556/DKUM-96398
Recommended citation for this dataset:
Murks Nina,
Kutoš Leon, Holobar Aleš, Divjak Matjaž, McGeady Ciaran,
Farina Dario, Kramberger Matej. “High-density surface EMG +
EEG recordings of isometric ankle dorsiflexion, imaginary
movement and GO/NO-GO tasks (HybridNeuro project)”. 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=96398
DATA & FILE OVERVIEW
This dataset was prepared in the context of the HybridNeuro
project (https://www.hybridneuro.feri.um.si/). It contains a
collection of simultaneous EMG and EEG recordings from 20
healthy volunteers. The main purpose of data collection was to
assess the sensitivity of corticomuscular coherence detection
estimated by the proposed motor unit-based EEG filters and
compare it to the classical EEG processing techniques.
Surface EMG was recorded from the tibialis anterior muscle
during isometric ankle dorsiflexions using 13 x 5 electrode
array with 8mm interelectrode distance (model GR08MM1305 from
OT Bioelettronica, Torino, Italy).
EEG was recorded with a 64 channel EEG cap (model EEG-Cap64
from OT Bioelettronica, Torino, Italy).
Both EMG and EEG were recorded simultaneously using the same
amplifier (model Quattrocento from OT Bioelettronica, Torino,
Italy) at 16 bit resolution and 2048 Hz sampling frequency.
For each participant, 25 tasks were recorded with total length
of approximately 37 minutes. Each task's data is stored in a
separate file in Matlab format (.MAT). Total dataset size is
26.9 GB.
- README.txt: this text file containing description of the dataset
- README.pdf: formatted PDF file containing description of the dataset
- metadata.xml: Dublin Core metadata in XML format
- compressed (ZIPed) files "S1.zip" to "S20.zip" containing
data folders for 20 different participants, each folder
contains the same set of 25 files:
- block1.mat: data for GO/NO-GO block 1
- block2.mat: data for GO/NO-GO block 2
- mvc1.mat: data for maximal voluntary contractions (MVC) 1
- mvc2.mat: data for maximal voluntary contractions (MVC) 2
- ramp10_1.mat: data for dorsiflexion at 10% MVC, set 1
- ramp10_2.mat: data for dorsiflexion at 10% MVC, set 2
- ramp20_1.mat: data for dorsiflexion at 20% MVC, set 1
- ramp20_2.mat: data for dorsiflexion at 20% MVC, set 2
- ramp20_imaginary.mat: data for imagined dorsiflexion at 20% MVC
- ramp30_1.mat: data for dorsiflexion at 30% MVC, set 1
- ramp30_2.mat: data for dorsiflexion at 30% MVC, set 2
- ramp40_1.mat: data for dorsiflexion at 40% MVC, set 1
- ramp40_2.mat: data for dorsiflexion at 40% MVC, set 2
- ramp50_1.mat: data for dorsiflexion at 50% MVC, set 1
- ramp50_2.mat: data for dorsiflexion at 50% MVC, set 2
- rest_eyes_closed_1.mat: data for rest with eyes closed, set 1
- rest_eyes_closed_2.mat: data for rest with eyes closed, set 2
- rest_eyes_closed_3.mat: data for rest with eyes closed, set 3
- rest_eyes_closed_4.mat: data for rest with eyes closed, set 4
- rest_eyes_closed_5.mat: data for rest with eyes closed, set 5
- rest_eyes_closed_6.mat: data for rest with eyes closed, set 6
- rest_eyes_closed_7.mat: data for rest with eyes closed, set 7
- sin_1.mat: data for dorsiflexion with sinusoidal force profile, set 1
- sin_2.mat: data for dorsiflexion with sinusoidal force profile, set 2
- sin_imaginary.mat: data for imagined dorsiflexion with sinusoidal force profile - participant_info.csv: table in CSV format with anonimized participant information (ID, gender, age, height, weight)
- figure1_protocol_steps.png: figure depicting all the steps of the experimental protocol
- figure2_go_nogo_ramp with visual_cues.png: figure depicting the required contraction levels and visual cues used during the GO/NO-GO task
- figure3_go_nogo_visuals.png: figure depicting an example of visual feedback provided for the participant during the GO/NO-GO task
- figure4_mat_file_content.png: screenshot of the Matlab workspace with all the variables loaded from one MAT file
- figure5_eeg_channels.png: figure depicting relations between EEG channel labels and EEG data matrix row numbers
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:
Description of methods used for collection/generation of data:
20 healthy participants volunteered to take part in the study,
including 12 men and 8 women. The men had a mean age of 34.8 ±
8.9 years, a height of 182.5 ± 10.0 cm, and a body mass of
89.6 ± 13.0 kg. The women had a mean age of 30.3 ± 11.2 years,
a height of 171.6 ± 5.3 cm, and a body mass of 72.0 ± 11.1 kg.
The experimental protocol was conducted at the Faculty of
Electrical Engineering and Computer Science, University of
Maribor and was approved by the Medical Ethics Committee of
the Republic of Slovenia (No. 0120-340/2023/3).
Environment: Participants were seated in an ankle dynamometer
(Wise Technologies, Ljubljana, Slovenia) equipped with a force
sensor (Type 1, 200 kg, AEP Transducers, Cognento, Italy),
with the hips, knees, and ankles flexed at 90° angle. The
lateral malleolus was aligned with the dynamometer's axis of
rotation. Plantarflexion movement was restricted using the
dynamometer's fixation system, which applied a blockade to the
thigh just proximal to the knee joint. The foot was
additionally secured with straps placed over the metatarsal
bones.
Participants were instructed to remain relaxed, with the
forearms resting on the dynamometer supports. To minimize
signal variability, they were asked to keep their head
position constant and to avoid clenching their teeth or
squeezing their hands. Visual feedback of the produced force
and trigger signals were provided via a monitor positioned in
front of the participant at a comfortable viewing height.
Preparation: All participants were instructed to abstain from
alcohol and caffeine on the day preceding and the day of the
experimental protocol. They were also asked to wash their hair
without using any hair products one day before testing and to
shave the right leg over the tibialis anterior muscle.
The tibialis anterior muscle was first identified by asking
participants to perform a brief ankle dorsiflexion. The skin
over the muscle belly was then prepared using an abrasive
paste (Spec Medica Everi). Surface electrodes were prepared by
placing an adhesive foam and filling the foam cavities with
Ten20 conductive paste. The electrode was positioned over the
muscle belly and secured using velcro straps. Two reference
straps - one serving as the patient reference and the other as
the preamplifier reference - were placed around the ankle.
EMG signals were displayed in real time on a computer screen
for visual inspection. First quality check was performed with
the participant in a relaxed state, during which the signals
were visible as flat lines on all channels. Participant then
performed a slight dorsiflexion contraction, resulting in
clearly visible motor unit action potentials across all
channels, which allowed for the observation of signal
propagation.
Prior to EEG cap placement, head dimensions were measured from
ear to ear and from nasion to inion to ensure accurate
positioning. The cap was positioned by aligning the Cz
electrode at the midpoint of both measurements. All electrodes
were then filled with conductive gel (Neurogel, Spec Medica).
Using Sessantaquatro amplifier (OT Bioelettronica, Torino,
Italy) impedance was monitored and reduced to below 5 kΩ. The
reference electrode (CDES003545, Spes Medica) was placed on
the collarbone. EEG signal quality was verified by visual
inspection for artifacts, including eye blinks, vertical and
horizontal eye movements, and teeth clenching. Signal quality
was further confirmed during a resting state with eyes closed
by identifying the presence of alpha activity.
Instrument- or software-specific information needed to interpret the data: We used the following acquisition settings for the Quattrocento amplifier:
- acquisition mode: monopolar
- sampling frequency: 2048 Hz
- A/D resolution: 16 bits
- EMG band pass filter: 10 - 500 Hz
- EEG band pass filter: 0.7 - 130 Hz
Quality-assurance procedures performed on the data: Signal quality was visually checked and adjusted for each participant at the start of the protocol. For EMG we visually checked for flat signal lines on all electrodes during relaxed state and for clearly visible motor unit action potentials across all channels during a slight dorsiflexion. For EEG we visually checked for presence of clearly visible artifacts (eye blinks, vertical and horizontal eye movements, teeth clenching) and for presence of alpha activity during resting state with eyes closed. No additional quality assurance steps were performed after the acquisition.
DATA-SPECIFIC INFORMATION FOR ALL .MAT FILES:
Variable list:
- adBits: double number. Resolution of the amplifier's A/D converter for data acquisition, in bits.
- eeg: 64 x N double array. Recorded EEG channels, one channel in one row. Each row contains N samples (double values) representing EEG voltage. N = total number of samples.
- eegCap: string. Name of the cap model used for recording EEG.
- eegHPF: double number. High pass filter cutoff frequency for EEG, in Hz.
- eegLPF: double number. Low pass filter cutoff frequency for EEG, in Hz.
- emg: 13 x 5 cell array. Recorded EMG signals: 13 rows x 5 columns. Each cell contains a 1 x N vector of samples (double values) representing EMG voltage. N = total number of samples.
- emgElectrode: string. Name of the electrode model used for recording high density surface EMG.
- emgHPF: double number. High pass filter cutoff frequency for EMG, in Hz.
- emgIED: double number. Inter-electrode distance of the surface electrode grid used to record EMG, in millimeters.
- emgLPF: double number. Low pass filter cutoff frequency for EMG, in Hz.
- emgMode: string. Name of the amplifier's EMG signal acquisition mode used.
- emgMuscle: string. Name of the muscle from which surface EMG was recorded.
- emgSide: string. On which side limb the EMG electrode was mounted: left or right.
- fSamp: double number. Amplifier sampling rate for data acquisition, in Hz.
- refAcquired: The exerted force measured by the force sensor, acquired from OTBioelettronica GUI, sampled at fSamp with amplitude in volts.
- refAux:The exerted force measured by the force sensor, acquired from AUX, sampled at fSamp expresesed in volts.
- refColorPoints: vector of double numbers. Labelled samples denoting the colours of the circles that were shown to the participant during the GO/NO-GO task: 1-blue, 2-yellow, 3-orange, 4-red, 5-green. Only available in the block1.mat and block2.mat files.
- refPerformed: The exerted force measured by the force sensor, acquired from AUX, sampled at 16 Hz, interpolated to fSamp and expresesed in % MVC.
- refRequested: The requested (target) force level, acquired from OTBioelettronica GUI, sampled at 16 Hz, interpolated to fSamp and expressed in % MVC.