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192 results for “EMG”
Data from: EMG versus torque control of human-machine systems: equalizing control signal variability does not equalize error or uncertainty
In this paper we asked the question: if we artificially raise the variability of torque control signals to match that of EMG, do subjects make similar errors and have similar uncertainty about their movements? We answered this question using two experiments in which subjects used three different control signals: torque, torque+noise, and EMG. First, we measured error on a simple target-hitting task in which subjects received visual feedback only at the end of their movements. We found that even when the signal-to-noise ratio was equal across EMG and torque+noise control signals, EMG resulted in larger errors. Second, we quantified uncertainty by measuring the just-noticeable difference of a visual perturbation. We found that for equal errors, EMG resulted in higher movement uncertainty than both torque and torque+noise. The differences suggest that performance and confidence are influenced by more than just the noisiness of the control signal, and suggest that other factors, such as the user's ability to incorporate feedback and develop accurate internal models, also have significant impacts on the performance and confidence of a person's actions. We theorize that users have difficulty distinguishing between random and systematic errors for EMG control, and future work should examine in more detail the types of errors made with EMG control.
Supplementary material for EMG of the Fugl-Meyer tasks
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Surface EMG data from seven representative forearm areas during seven grasps relevant for personal autonomy
<p>This dataset contains 3150 data of four sEMG characteristics: muscle activity, zero crossing, enhanced wavelength and enhanced mean absolute value. while performing seven grasp types. Data were recorded using surface EMG electrodes from seven representative forearm areas. </p> <p>Data are presented as a Matlab data structure (<em>.mat</em> file). This structure contains the four sEMG characteristics classified as: subject, grasp, repetition and spot area. The fields contained in the structure are those detailed in the following scheme:</p> <ul> <li>Subject: subject ID;</li> <li>Grasp: Grasp ID;</li> <li>Repetition: Repetition of each grasp, from 1 to 3.</li> <li>Spot area: Spot ID for the seven representative spot areas, according to [1].</li> <li>FZC: zero crossing</li> <li>EWL: enhanced wavelength</li> <li>EMAV: enhanced mean absolute value</li> <li>AM: Muscle activity</li> </ul> <p>[1] 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>
Feasibility and effectiveness of a full body biofeedback mirror on musicians: EMG data + Music habits questionnaire
<p>Feasibility and effectiveness of a full body biofeedback mirror on musicians: EMG data + Music habits questionnaire</p>
EMG from forearm datasets for hand gestures recognition
<p>This dataset contains 2 sets of sEMG recordings: a set containing <strong>PINCH</strong> movements (4 pinches between thumb and index/middle/ring/pinky finger) and a set containing <strong>ROSHAMBO</strong> movements (3 movements: rock, paper, scissors). Both sets have been recorded with the Myo armband. The Myo is composed of 8 equally spaced non-invasive sEMG sensors that can be placed approximately around the middle of the forearm. The sampling frequency of Myo is 200 Hz. The output of the Myo is a.u.. The <strong>PINCH </strong>set contains recordings of 22 subjects whilst the <strong>ROSHAMBO</strong> set contains recordings of 10 subjects. Each subject performed 3 sessions, where each hand gesture was recorded 5 times, each lasting for 2s. Between the gestures a relaxing phase of 1s is present where the muscles could go to the rest position, removing any residual muscular activation. Full details for the <strong>ROSHAMBO</strong> set can be found in:</p> <p>Donati, Elisa, et al. "Processing EMG signals using reservoir computing on an event-based neuromorphic system." <em>2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)</em>. IEEE, 2018.</p> <p>For each session, the dataset contains 2 <strong>*.npy</strong> files one specifying the EMG data (<strong>*_emg.npy</strong>) the other one (<strong>*_ann.npy</strong>) specifying the corresponding gestures along the sampled EMG. The data can be easily loaded in python with numpy.</p> <p> </p>
EMG Control Assistance Virtual Reality Interface Coupled With Cerebellar-iTBS for Arm Recovery After Stroke (ERICA)
ClinicalTrials.gov study NCT06911671. IPD Sharing: NO. Countries: 1. Publications: 6.
Neuromuscular Monitoring:TOFCuff Versus EMG
ClinicalTrials.gov study NCT04282213. IPD Sharing: NO. Countries: 1. Publications: 5.
Posterior Chain EMG Patterns in Athletes With and Without Hamstring Injury
ClinicalTrials.gov study NCT07171385. IPD Sharing: YES. Countries: 1. Publications: 1.
Effects of Cervical Traction With and Without EMG Biofeedback in Patients With Cervical Radiculopathy
ClinicalTrials.gov study NCT05352464. IPD Sharing: NO. Countries: 1. Publications: 0.
Validation of Triggered EMG Values for Pedicle Screws Using a Powered Screwdriver
ClinicalTrials.gov study NCT02064101. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Intermittent Hypoxia and Upper Extremity EMG Recordings in Individuals With Spinal Cord Injury
ClinicalTrials.gov study NCT05513911. IPD Sharing: NO. Countries: 1. Publications: 0.
The Surface EMG Biofeedback as an Alternative Therapy to Reduce Chronic Low Back Pain
ClinicalTrials.gov study NCT06306833. IPD Sharing: NO. Countries: 1. Publications: 12.
Multi-Sensory Training for Balance, EMG, Sensory Deficits in DPN
ClinicalTrials.gov study NCT07096570. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Effects of Thai Massage on EMG
ClinicalTrials.gov study NCT02067325. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effect of Auricular Vagus Nerve Stimulation on EEG and EMG Measurement in Healthy Persons
ClinicalTrials.gov study NCT05088135. IPD Sharing: NO. Countries: 1. Publications: 1.
EMG for Uterotonic Efficiency Estimation
ClinicalTrials.gov study NCT04201665. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of EMG Biofeedback in Female Patients With Stress Urinary Incontinence
ClinicalTrials.gov study NCT05366426. IPD Sharing: NO. Countries: 1. Publications: 2.
Study on the Effects of an EMG-controlled Functional Electrical Stimulator for Upper Limb for Post-stroke Patients
ClinicalTrials.gov study NCT06928857. IPD Sharing: YES. Countries: 1. Publications: 31.
Surface EMG and Ultrasound in MND
ClinicalTrials.gov study NCT05314348. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
EMG-controlled Virtual Reality to Improve Upper Extremity Function in Chronic Stroke Patients
ClinicalTrials.gov study NCT04154371. IPD Sharing: NO. Countries: 1. Publications: 16.
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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.
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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.