Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
192
datasets available to search
ShareScore release 0.9.0
Dataset results
192 results for “EMG”
Spatial variation and inconsistency between estimates of onset of muscle activation from EMG and ultrasound
<p>Study abstract: Delayed onset of muscle activation is a descriptor of impaired motor control. Activation<br> onset can be estimated from electromyography (EMG)-registered muscle excitation and<br> from ultrasound-registered muscle motion, which enables non-invasive measurements in<br> deep muscles. However, in voluntary activation, EMG- and ultrasound-detected activation<br> onsets may not correspond. To evaluate this, ten healthy men performed isometric elbow<br> flexion at 20% to 70% of their maximal force. Utilising a multi-channel electrode<br> transparent to ultrasound, EMG and M(otion)-mode ultrasound were recorded<br> simultaneously over the biceps brachii muscle. The time intervals between automated and<br> visually estimated activation onsets were correlated with the regional variation of EMG<br> and muscle motion onset, contraction level and speed. Automated and visual onsets<br> indicated variable time intervals between EMG- and motion onset, median (interquartile<br> range) 96 (121) ms and 48 (72) ms, respectively. In 17% of trials (computed analysis) or<br> 23% (visual analysis), motion onset was detected before local EMG onset. Multi-channel<br> EMG and M-mode ultrasound revealed regional differences in activation onset, which<br> decreased with higher contraction speed (Spearman ρ≥0.45, P<0.001). In voluntary<br> activation the heterogeneous motor unit recruitment together with immediate motion<br> transmission may explain the high variation of the time intervals between local EMG- and<br> ultrasound-detected activation onset.</p> <p>Data description:</p> <p><strong>EMG data:</strong> folder includes the EMG, torque and synchronization signal data as .otb files. The respective program can be downloaded without costs from http://www.otbioelettronica.it/index.php?lang=en. Data consist of two series (Misome2 and Misome3) of isometric trials at different force levels. The first three digits refer to the subject number.</p> <p><strong>M-mode ultrasound data</strong>: folder includes the M-mode clips of all recorded trials in .tvd format. The respective program can be downloaded for free at http://www.telemedultrasound.com/download/software-downloads/?lang=en. In addition, images of activation onset in DICOM format are provided. The data are sorted for subjects and series (Misome2 and Misome3). The following explanation refers to the filenames of the DICOM images. Usually, the M-mode trace started at the left side synchronously with the synch signal. In this case the rightmost frame of the trace is missing to indicate that the left edge represents the start of the trace. If the file name includes “ons50”, the frame includes the 50. frame = the rightmost frame, still starting with the synch signal. If the filename includes on100, the rightmost frame is the 100. frame and the duration of 50 frames must be added to the visible onset time. Filenames that include “basel” refer to frames that represent a proper delineation of the baseline.</p> <p><strong>Excel data sheet</strong>: Data sheet that includes the computed and visual EMG, M-mode ultrasound and torque onsets, the rate of torque development and the differences between the different types of onsets. The column headers explain the data type, most comprehensively in the first computed data sheet. The colors facilitate the orientation with blue columns referring to torque onsets and green columns referring to ultrasound onsets. The violet EMG channels are those in vicinity to the ultrasound beam. In the second version of each sheet the 5% slowest trials are separated.</p>
MOVMUS-UJI Dataset & ERGOMOVMUS: EMG and kinematics data of the hand in activities of daily living with special interest for ergonomics
<p>A dataset of <strong>human hand kinematics</strong> and <strong>forearm muscle activation</strong> collected during the performance of a wide variety of activities of daily living (ADLs) is presented, with tagged characteristics of products and tasks. A total of <strong>26 participants</strong> performed <strong>161 ADLs</strong>, selected to be representative of common elementary tasks, grasp types, product orientations and performance heights. 105 products were used, being varied regarding shape, dimensions, weight and type (common products and assistive devices).</p> <p>The data were recorded using CyberGlove instrumented gloves on both hands measuring 18 degrees of freedom on each and seven surface EMG sensors per arm recording muscle activity. The products and their arrangement were the same across subjects, and tasks were performed in a guided way. Data of <strong>more than 4100 ADLs</strong> is presented in this dataset as <strong>Matlab structures</strong> with full continuous recordings, which may be used in applications such as machine learning or to characterize healthy human hand behaviour.</p> <p>The dataset is accompanied with a <strong>custom data visualization application (ERGOMOVMUS)</strong> as a tool for ergonomics applications, allowing visualization and calculation of aggregated data from specific task, product and/or subjects’ characteristics.</p> <p> </p> <p><strong>v3.0 includes the following updates:</strong></p> <p>- Statistical summary of the recordings both in .xlsx and .ods file format (v1.1 only included it in .xlsx file format)</p> <p>- Updated experiment details in "MOVMUS-UJI DATASET GUIDE.pdf".</p>
Comprehensive Kinetic and EMG Dataset of Daily Locomotion with 6 types of Sensors
<p> </p> <p><strong><a href="https://www.cambridge.org/core/journals/wearable-technologies/article/wearable-realtime-kinetic-measurement-sensor-setup-for-human-locomotion/488C21B7706FFDFA7FFAB387FD0A1A64?utm_campaign=shareaholic&utm_medium=copy_link&utm_source=bookmark">The paper</a> is now published in the recent Wearable Technology Journal, more detailed information on this dataset can be found there!</strong></p> <p>A human movement experiment with 12 young adults performing 13 daily movement trials (6 walking trials (speed: 0.9, 1.8, 2.7, 3.6, 4.5, 5.4 km/ h; 3 running trials (speed: 6.3, 8.8, 9.9 km/h); and four non-locomotion trials (vertical jump, squat, lunge, and single leg landing) was conducted with the ethic approved by the University of Twente (ET/A.21.19298, reference number 2021.57). </p> <p>Six types of measurement devices were used to capture different information of participants’ movements. They can be divided into two systems: the wearable system and the conventional non-wearable system. In the wearable measurement system, eight IMUs (Xsens Link, Enschede, The Netherlands) were used to measure the kinematic movements of lower limbs and trunk. A pair of pressure insoles (Moticon, Munich, Germany) was used to measure the vertical GRF and CoPs. In the conventional system, an optical motion capture system (OMC) containing 8 infrared light cameras (6+ series, Qualisys, Gothenburg, Sweden) was used to measure body kinematics data using reflective markers. A split-belt instrumented treadmill (Motek-Forcelink B.V, Culemborg, The Netherlands) was used to measure the GRFs under each foot. Two video cameras were also included inside the conventional system to capture the RGB images of participants’ body postures at the sagittal and frontal planes (<a href="https://doi.org/10.5281/zenodo.6644593">https://doi.org/10.5281/zenodo.6644593</a>). In addition, nine electromyography sensors (EMGs) (Delsys Trigno, Delsys, USA) were included to record the activations of nine major muscles in the dominant leg ("soleus", "medial gastrocnemius", "lateral gastrocnemius", "tibialis anterior", " semimembranosus", " biceps femoris long head", "vastus lateral", "rectus femoris", "vastus medial").</p> <p>In this shared data repository, both raw data (to be uploaded) and processed data (Processed_data.zip) are provided. The data processing pipeline can be found in this public GitHub repository: <a href="https://github.com/HuaweiWang/BioMechPro-WearableSystemVaildation">https://github.com/ET-BE/BioMechPro/tree/study/WearableSystemValidation</a>. Guidelines for creating the same wearable system in the corresponding comparison study are shared in this GitHub repo: <a href="https://github.com/HuaweiWang/WearableMeasurementSystem">https://github.com/HuaweiWang/WearableMeasurementSystem</a>.</p> <p><strong>[Note!]</strong> If you have unstable network that not able to download the huge data files, please check this version of dataset with small file sizes(2GB each)</p> <ul> <li>Raw data: <a href="https://doi.org/10.5281/zenodo.7422043">https://doi.org/10.5281/zenodo.7422043</a></li> <li>Processed data: <a href="https://doi.org/10.5281/zenodo.7422031">https://doi.org/10.5281/zenodo.7422031</a> </li> </ul> <p> </p> <p>Dataset structure descriptions:</p> <p><a href="https://zenodo.org/api/files/871df791-82f9-495a-ac56-eafd195c56ad/Raw_data.rar?versionId=41a0df36-b9b2-4e48-a50e-a6e81b23609b">Raw_data.rar</a>: Raw dataset</p> <ul> <li><em>Subjxx</em>: subject folder <ul> <li><em>Qualisys</em>: Qualisys project folder of the recordings</li> <li><em>Xsens</em>: Xsens project folder of the recordings</li> <li><em>Insoles</em>: Pressure insole project folder of the recordings</li> </ul> </li> </ul> <p><a href="https://zenodo.org/api/files/871df791-82f9-495a-ac56-eafd195c56ad/Processed_data.rar?versionId=d035b581-a334-48bb-88e1-3c655943b5cc">Processed_data.rar</a>: Processed dataset.</p> <ul> <li><em>allAverage.mat</em>: the overall summarization data of all subject at all movement trials. </li> <li><em>dataValidation.m</em>: the Matlab code to plot the summarization data by loading the allAverage.mat.</li> <li><em>subjs_info.txt</em>: general information of all participants. </li> <li><em>ComparisonPlots</em>: folder that contains the comparison plots between the laboratory-based system and the wearable measurement systems.</li> <li><strong><em>Subjxx</em></strong>: processed data for participants xx <ul> <li><em>dynMVCvalue.mat</em>: the dynamic Maximal Voluntary Contraction of measured muscles (highest value among all recorded movements)</li> <li><em>MVCvalue.mat</em>: the Maximal Voluntary Contraction of measured muscles (highest value in MVC recording trial only)</li> <li><em>Subjxx_xxxx_xx.mat</em>: the processed data (generated by the above mentioned processing pipeline) of current subject at specific movement trial.</li> <li><em>Qualisys</em>: The exported mat files from the Qualisys recordings, including markers, EMGs, GRFs.</li> <li><em>Xsens</em>: The exported .mvnx files from the Xsens MVN software reprocessing. This file can be directly loaded by Matlab without requiring the Xsens license.</li> <li><em>Insole</em>: Insole recorded data, parsed from the Moticon endpoint SDK output.</li> <li><em>OS</em>: The folder that contains the scaled OpenSim model and corresponding .xml and data files for IK and ID processing. Majority content in this folder is automatically generated by the processing pipeline</li> <li><em>Figures</em>: plots of the processed data, including joint angles, GRFs, joint torques, and EMGs. They are all generated in the last module of the processing pipeline.</li> </ul> </li> </ul> <p>Structure of the <strong>Subjxx_xxxx_xx.mat</strong>:</p> <p><em><strong>Subjxx_xxxx_xx</strong>.</em><em><strong>mat:</strong></em></p> <ul> <li><em>Info</em>: the general information of the participant and corresponding processing steps.</li> <li><em>Marker</em>: Marker data from Qualisys</li> <li><em>Force</em>: GRFs data from Qualisys</li> <li><em>EMG</em>: EMG data from Qualisys</li> <li><em>IMU</em>: Motion data from Xsens IMU system (from .mvnx)</li> <li><em>Insole</em>: The recorded pressure insole data</li> <li><strong>Resample</strong>: This data structure that contains the resampled data of the above mentioned sensor data <ul> <li><em>FrameRate</em>: the sampling rate for all resampled sensor data</li> <li><em>Marker</em>: Resampled marker data</li> <li><em>Force</em>: resampled force data</li> <li><em>EMG</em>: resampled EMG data</li> <li><em>IMU</em>: resampled IMU data</li> <li><em>Insole</em>: resampled Insole data</li> <li><em>CoM</em>: resampled center of mass data from Xsens IMU system</li> <li><strong>Sych</strong>: this data structure contains the IK & ID data of two measurement systems. They are also synchronized by calculate the highest correlation coefficient. <ul> <li><em>DeltaT</em>: the time differences between the laboratory-based system and the wearable measurement system.</li> <li><em>IKAngData</em>: the joint angle data from marker data inverse kinematics</li> <li><em>ForcePlateGFRData</em>: the ground reaction force data from instrumented treadmill</li> <li><em>IDTrqData</em>: the joint torque data from laboratory measurement system (optical + treadmill)</li> <li><em>IMUAngData</em>: the joint angle data from Xsens MVN software</li> <li><em>InsoleGRFData</em>: the ground reaction force data from pressure insoles</li> <li><em>IDTrqData_portable</em>: the joint torque data from the wearable system inverse dynamics</li> <li><em>EMG</em>: synchronized EMG data</li> <li><em>CoM</em>: synchronized CoM data</li> <li><em>xxxxxLabel</em>: the labels of each data column of corresponding data matrix</li> <li><strong>Average</strong>: this data structure contains the averaged gait/moment cycles <ul> <li><em>hsMatrix_right</em>: The heel strike data points of the right leg</li> <li><em>hsMatrix_left</em>: the heel strike data points of the left leg</li> <li><em>EMGAvedynNorFlag</em>: whether dynamic MVC normalization is applied on EMG.</li> <li><em>EMGAveNorFlag</em>: whether MVC normalization is applied on EMG.</li> <li><em>xxxx</em>: The averaged data of corresponding variables</li> <li><em>ForcePlateGRFDataInCalCn</em>: transferred treadmill GRF data from the treadmill global coordinate to the local Calcaneus coordinate of the scaled OpenSim model. </li> </ul> </li> </ul> </li> </ul> </li> </ul>
A dataset for the investigation of upper limb torque prediction from EMG signals
<h1>Motivation</h1> <div>EMG-driven exoskeleton assistance requires the use of intention detection models to associate electromyographic signals with some feature of human movement, such as angular position, velocity, or joint torque. The goal of this dataset is to provide data that allows the benchmarking of such models for a variety of movements.</div> <h1>Short description</h1> <div>This dataset includes kinematic, dynamic and electromyographic data from 17 participants (11 males, age 28.2 ± 7 years, height 175.4 ± 7 cm, weight 70 ± 11 kg). These data were collected during the performance of a sagittal plane upper limb tracking task for single joint (elbow flexion/extension) and multiple joint (elbow and shoulder flexion/extension).</div> <h1>Methodology</h1> <div>A detailed description of the data collection methodology can be found here: https://www.biorxiv.org/content/10.1101/2024.01.11.575155v1</div> <h1>Data Description</h1> <div>The data set consists of 17 folders, one for each participant. Inside each folder you will find</div> <div>- A metadata file (<strong>SXX.json</strong>) containing information about the subject: age, sex, weight, height, and upper limb masses and lengths.</div> <div>- An OpenSim model file (<strong>scaledModel.osim</strong>) containing a scaled upper limb model of the given subject.</div> <div>- A <strong>MVC</strong> folder containing EMG data from maximal voluntary contraction trials</div> <div>- A <strong>SJ</strong> folder containing trial folders for the single joint condition (elbow flexion/extension).</div> <div>- A <strong>MJ</strong> folder containing test folders for the multi-joint condition (elbow and shoulder flexion/extension)</div> <h2>EMG Data</h2> <div>The files containing EMG data have the following header</div> <div><code>TIME,DELTAnt,DELTMed,DELTPost,PECT,LATI,TRILong,TRILat,TRIMed,BICLong,BICShort,BRA,BRD</code></div> <div>This corresponds to a time stamp and EMG signals from the anterior, median and posterior detloids, pectoralis major, latissimus dorsi, long, lateral and median triceps, long and short biceps, brachioradialis and brachialis.</div> <h2>MVC data</h2> <div>The MVC folder contains two files: <strong>emgMVCElbow.csv</strong> and <strong>emgMVCShoulder.csv</strong>. They were collected during the realisation of maximum voluntary contraction tasks and contain raw EMG data sampled at 2 kHz.</div> <h2>Trial data</h2> <h3>Single-joint condition</h3> <div>Single-joint trials contain 5 files:</div> <div>- <strong>emgFilt.csv</strong>: Filtered EMG signals, using a 20-450 Hz bandpass filter, a rectification, a 3Hz lowpass filter and normalized with MVC. Sampled at 100 Hz.</div> <div>- <strong>emgRaw.csv</strong>: Raw EMG signals. Sampled at 2 kHz.</div> <div>- <strong>humanPositions.csv</strong>: Angular position of the elbow in rad. Sampled at 100 Hz.</div> <div>- <strong>humanVelocities.csv</strong>: Angular velocity of the elbow in rad/s. Sampled at 100 Hz.</div> <div>- <strong>muscleTorque.csv</strong>: Joint torque of the elbow in N.m. Sampled at 100 Hz.</div> <h3>Multi-joint condition</h3> <div>Multi-joint trials contain 5 files:</div> <div>- <strong>emgFilt.csv</strong>: Filtered EMG signals, using a 20-450 Hz bandpass filter, a rectification, a 3Hz lowpass filter and normalized with MVC. Sampled at 100 Hz.</div> <div>- <strong>emgRaw.csv</strong>: Raw EMG signals. Sampled at 2 kHz.</div> <div>- <strong>humanPositions.csv</strong>: Angular positions of the upper limb in rad. Sampled at 100 Hz.</div> <div>- <strong>humanVelocities.csv</strong>: Angular velocities of the upper limb rad/s. Sampled at 100 Hz.</div> <div>- <strong>muscleTorque.csv</strong>: Joint torques of the upper limb in N.m. Sampled at 100 Hz.</div> <div>For this trial, kinematic and torque files use the following headers:</div> <div><code>TIME,elv_angle,shoulder_elv,shoulder_rot,elbow_flexion,pro_sup,deviation,flexion</code></div> <div>The columns correspond to the OpenSim model coordinates. For sagittal plane movement, columns of interest are <strong>shoulder_elv</strong> for shoulder flexion/extension and <strong>elbow_flexion</strong> for elbow flexion/extension.</div>
Simulated EEG and EMG data for Reference Phase Analysis evaluation.
<p><span>This dataset comprises simulated EEG (Electroencephalography) signals recorded from 29 channels along with an EMG (Electromyography) signal. The signals have been artificially generated using real EEG and EMG signals as the basis.</span></p> <p><span>Five distinct source models were generated, each comprising a different number</span><span> of <span>sources. We created models with 2, 3, 4, 5, and 6 sources</span>. Furthermore, we add additive noise to the signals encompassing</span> <span>SNRs ranging from 90 to 0 dB and a phase jitter ranging from 0.1 to 1.5 radians.</span></p> <p><span>We generated EEG and EMG signals using MATLAB and employed a forward model, created with FieldTrip, to convert signals from the source space to the electrode space. EEG measures were computed utilizing a linear mixture model.</span></p> <p><span>The simulated dataset aims to mimic the characteristics of real EEG and EMG signals, including their temporal dynamics, noise, and frequency spectra. It serves as a valuable resource for developing and testing signal processing algorithms, and brain-computer interfaces in the fields of neuroscience and biomedical engineering.</span></p>
PsPM-LSOA: Pupil size response, SCR, EMG, ECG and respiration measurement from a classical (Pavlovian) discriminant delay fear conditioning task
<p>This dataset includes eye tracker (including pupillometry), skin conductance, EMG, ECG and respiratory measurements. Also included are task information, keypress responses, keypress response times and key correctness for each of 22 healthy unmedicated participants (13 females and 9 males aged 25.3 +/- 4.3 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. CS consists of two sine tones with constant frequency (135 Hz or 300 Hz). Assignment of CS frequency to CS-/CS+ is randomised across participants. US is a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. SOA betwen the CS and US is 7.5 s. The ITI is randomly determined on each trial to be 10.0 +/- 1.4 s.</p>
EMG hand gesture dataset
<p>This dataset contains surface electromyography (sEMG) data of 5 different hand gestures performed by eight subjects. The data is store in a folder for each one of the subjects, each folder, contains 5 files, one for each gesture. The files contain the sEMG data of 4 sEMG channels placed on the forearm. The gestures are: open hand, closed hand, lateral pinch, signaling sign, rock sign. A file with a demo Recurrent Neural Network is also included.</p> <p>The reader may refer to the following articles for more information about the dataset: <a href="https://doi.org/10.3390/app12199700">LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals</a> and <a href="https://doi.org/10.3390/biomimetics8010029">A Proposal of Bioinspired Soft Active Hand Prosthesis</a>.</p>
Toward Early and Objective Hand Osteoarthritis Detection by using EMG during grasps
<p>Dataset analyzed in the study "Toward Early and Objective Hand Osteoarthritis Detection by using EMG during grasps". Use of the data requires proper reference to [1].</p> <p>Dataset contains Electromyographic data from forearm, recorded with an 8-channel sEMG Biometrics Ltd. device. The fields contained in the structure are those detailed in the following scheme::</p> <ul> <li>Group: 0 for healthy subjects; 1 for HOA patients</li> <li>Subject: subject ID;</li> <li>Grasp: grasp ID, according to Figure 1 [1];</li> <li>Raw EMG data (7 columns): Raw sEMG data without any filter and not resampled, for the seven representative spot areas according to [2].</li> </ul> <p>[1] Jarque-Bou, N.J.; Gracia-Ibáñez, V.; Roda-Sales, A.; Bayarri-Porcar, V.; Sancho-Bru, J.L.; Vergara, M. Toward Early and Objective Hand Osteoarthritis Detection by Using EMG during Grasps. <em>Sensors</em> <strong>2023</strong>, <em>23</em>, 2413. https://doi.org/10.3390/s23052413</p> <p>[2] Jarque-Bou, N. J., Vergara, M., Sancho-Bru, J. L., Alba, R.-S. & Gracia-Ibáñez, V. Identification of forearm skin zones with similar muscle activation patterns during activities of daily living. <em>J. NeuroEngineering Rehabil. </em>(2018).</p> <p> </p>
EEG and EMG dataset for the detection of errors introduced by an active orthosis device (IJCAI'23 CC6 Competition)
<p>This dataset was a part of the IJCAI 2023 competition : CC6: IntEr-HRI: Intrinsic Error Evaluation during Human-Robot Interaction (<a href="https://ijcai-23.org/competitions/">IJCAI'23 Official Website</a>). This dataset repository is divided into 3 versions:</p> <ul> <li><strong><em>Version 1: </em>Training data + Metadata</strong></li> <li><strong><em>Version 2: </em>Test data</strong></li> <li><strong>Version 3: Complete dataset (EEG + EMG) </strong></li> </ul> <p><strong>For more detailed information about the competition, please visit our <a href="http://ijcai-23.dfki-bremen.de/competitions/inter-hri/">competition webpage</a>.</strong></p> <p>This dataset contains recordings of the electroencephalogram (EEG) data from eight subjects who were assisted in moving their right arm by an active orthosis. </p> <p>The orthosis-supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the orthosis was actively moving the subject's arm, some errors were deliberately introduced for a short duration of time. During this time, the orthosis moved in the opposite direction. The errors are very simple and easy to detect. EEG and EMG data are provided. The recorded EEG data follows the BrainVision Core Data Format 1.0, consisting of a binary data file (.eeg), a header file (.vhdr), and a marker file (.vmrk) (<a href="https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/%7D%7D.">https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/).</a> For ease of use, the data can be exported into the widely adopted BIDS format. Furthermore, for data analysis, processing, and classification, two popular options are available - MNE (Python) and EEGLAB (MATLAB). </p> <p><strong>If you use our dataset, cite our paper.</strong></p> <p>Frontiers in Human Neuroscience DOI: <a href="https://doi.org/10.3389/fnhum.2024.1304311">10.3389/fnhum.2024.1304311</a></p> <p>BibTeX citation:</p> <div> <div>@ARTICLE{10.3389/fnhum.2024.1304311,</div> <div>AUTHOR={Kueper, Niklas and Chari, Kartik and Bütefür, Judith and Habenicht, Julia and Rossol, Tobias and Kim, Su Kyoung and Tabie, Marc and Kirchner, Frank and Kirchner, Elsa Andrea},</div> <div>TITLE={EEG and EMG dataset for the detection of errors introduced by an active orthosis device},</div> <div>JOURNAL={Frontiers in Human Neuroscience},</div> <div>VOLUME={18},</div> <div>YEAR={2024},</div> <div>URL={https://www.frontiersin.org/articles/10.3389/fnhum.2024.1304311},</div> <div>DOI={10.3389/fnhum.2024.1304311},</div> <div>ISSN={1662-5161}</div> <div>}</div> </div>
Portable EMG-triggered Hand Robot for Individuals After Stroke
ClinicalTrials.gov study NCT02364700. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Effect of Specific Postural Corrective Exercises on EMG Activity in Patients With Forward Head Posture
ClinicalTrials.gov study NCT05895708. IPD Sharing: NO. Countries: 1. Publications: 6.
The Effect of Lumbar Stabilization Exercise and Gait Training on Lower Back Muscles- Electromyographic(EMG) Analysis
ClinicalTrials.gov study NCT02938169. IPD Sharing: NO. Countries: 1. Publications: 1.
EMG Training for Altering Activation Patterns After Stroke
ClinicalTrials.gov study NCT03619772. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Effect of Forward Head Correction on EMG of Masticatory Muscles in Patients With TMD
ClinicalTrials.gov study NCT05756010. IPD Sharing: NO. Countries: 1. Publications: 9.
Ultrasound and EMG Guided Botox Injection for the Treatment of Non-Relaxing Puborectalis Syndrome
ClinicalTrials.gov study NCT01780636. IPD Sharing: Not stated. Countries: 1. Publications: 15.
Treatment of Chronic Stroke With AMES + EMG Biofeedback
ClinicalTrials.gov study NCT01116544. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Enhancing the security of pattern unlock with surface EMG-based biometrics
Open the record for dataset details and reuse information.
A data archive including processed spiking data, raw EMG datasets, and video data during locomotion behavior from six mice
Open the record for dataset details and reuse information.
In vivo x-ray diffraction and simultaneous EMG reveal the timecourse of myofilament lattice dilation and filament stretch
<p>Muscle function within an organism depends on the feedback between molecular and meter-scale processes. Although the motions of muscle's contractile machinery are well described in isolated preparations, only a handful of experiments have documented the kinematics of the lattice occurring when multi-scale interactions are fully intact. We used time-resolved X-ray diffraction to record the kinematics of the myofilament lattice within a normal operating context: the tethered flight of Manduca sexta. As the primary flight muscles of M. sexta are synchronous, we used these results to reveal the timing of in vivo cross-bridge recruitment, which occurred 24 ms (s.d. 26) following activation. In addition, the thick filaments stretched an average of 0.75% (s.d. 0.32) and thin filaments stretched 1.11% (s.d. 0.65). In contrast to other in vivo preparations, lattice spacing changed an average of 2.72% (s.d. 1.47). Lattice dilation of this magnitude significantly affects shortening velocity and force generation, and filament stretching tunes force generation. While the kinematics were consistent within individual trials, there was extensive variation between trials. Using a mechanism-free machine learning model we searched for patterns within and across trials. Although lattice kinematics were predictable within trials, the model could not create predictions across trials. This indicates that the variability we see across trials may be explained by latent variables occurring in this naturally functioning system. The diverse kinematic combinations we documented mirror muscle's adaptability and may facilitate its robust function in unpredictable conditions.<br> <br> </p>
Kinematics and EMG to show integration of proprioceptive and visual feedback during online control of reaching
<p>Visual and proprioceptive feedback both contribute to optimal perceptual decisions, but it remains unknown how these feedback signals are integrated together or consider factors such as delays and variance during online control. We investigated this question by having participants reach to a target with randomly applied mechanical and/or visual disturbances. We observed that the presence of visual feedback during a mechanical disturbance did not increase the size of the muscle response significantly but did decrease variance, consistent with a dynamic Bayesian integration model (Experiment 1). In a control experiment we verified that vision had a potent influence when mechanical and visual disturbances were both present but opposite in sign (Experiment 2). These results highlight a complex process for multi-sensory integration, where visual feedback has a relatively modest influence when the limb is mechanically disturbed, but a substantial influence when visual feedback becomes misaligned with the limb. The dataset contains hand kinematics and EMG data recorded during each experiment, and information of visual/mechanical disturbances that were applied during the experiments.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.