22 results for "electromyography" · page 2 of 3 · ranked by relevance
BIDS-EMG dataset from Grison et al. 2025 - HDsEMG recordings of tibialis anterior during isometric contractions at 10-70% MVC, 1 subject, 128-channel grids (2x64), with concurrent intramuscular EMG ground truth labels (MUniverse benchmark)
…dual-layer electroencephalography (EEG), neck electromyography (EMG), inertial measurement unit (IMU) acceleration…
A multimodal EEG dataset comprising motor imagery and motor execution data from 25 healthy subjects performing 11 intuitive upper-limb movement tasks (6 reaching, 3 grasping, 2 wrist twisting) across 3 sessions. The dataset includes 71-channel EEG recordings (60 EEG, 4 EOG, 7 EMG channels) sampled at 1000 Hz with synchronized behavioral annotations, designed for brain-computer interface research and motor control applications. This is a BIDS-formatted derivative dataset converted from the original Jeong et al. 2020 publication using MOABB (Mother of All BCI Benchmarks).
…dual-layer electroencephalography (EEG), neck electromyography (EMG), inertial measurement unit (IMU) acceleration…
This dataset contains EEG recordings from 12 healthy human subjects performing three-class motor imagery tasks involving the same upper extremity: rest, imagined grasping, and imagined elbow flexion. Data were collected across four sessions per subject using a 32-channel EEG system, and the dataset is a BIDS-formatted derivative of the original study on classifying imaginary motor states using time-domain features. The dataset supports research in brain-computer interfaces (BCI) for motor rehabilitation applications.
[ from 15 healthy subjects across three sessions. The dataset comprises 60-channel EEG data sampled at 250 Hz with 450 trials per subject (150 trials per session across 3 sessions), designed to evaluate session-to-session transfer learning in brain-computer interface applications. Data were preprocessed with 60 Hz notch filtering and cue-aligned epoching, with the 4-second motor imagery window extracted for analysis.
This dataset contains multimodal brain–machine interface (BMI) recordings from seven healthy adults who trained over nine longitudinal sessions to control a lower-limb exoskeleton via motor imagery. It includes 60-channel EEG, 4-channel EOG, dual IMU motion data, and exoskeleton control/feedback signals collected during open-loop calibration and closed-loop walk/stop trials. The dataset supports research on EEG-based decoding of motor imagery for neurorehabilitation and human-robot interaction applications.
…as resting state, artifacts, and electromyography of both arms were also recorded…
…RSP*), electrodermal activity (*EDA*) and electromyography (*EMG*), were obtained at a sampling…