93 results for "EMG" · page 9 of 10 · ranked by relevance
This dataset contains EEG recordings collected as part of the NeuroTechs project examining STEM skills. Data were acquired using the Unicorn EEG device and converted to BIDS format, with signal values expressed in microvolts. The dataset is intended to support research on neural correlates of STEM-related cognitive processes.
This dataset contains laser-evoked potential recordings capturing neural responses in the human spinal cord and cortex following nociceptive laser stimulation. It was collected to investigate the neurophysiological processing of pain-related signals across spinal and cortical levels. The dataset is organized according to the BIDS standard for electrophysiological data.
The BNCI 2014-002 Motor Imagery dataset comprises EEG recordings from 14 healthy subjects performing two-class motor imagery tasks (right hand and feet imagination) in a cue-guided Graz-BCI paradigm. Data were acquired at 512 Hz using 15 EEG channels with online Butterworth filtering and Laplacian montage, yielding 160 trials per subject with continuous visual feedback. This minimally preprocessed dataset has been benchmarked for brain-computer interface applications using machine learning classifiers including random forests and regularized linear discriminant analysis.
This dataset contains simultaneous scalp and ear-EEG recordings from 6 healthy right-handed adults performing a motor execution task involving left and right hand fist clenching, cued by visual arrow stimuli. Recordings were made at 1000 Hz using a 122-channel Neuroscan SynAmps2 system, yielding 1114 trials, and are intended for benchmarking motor task classification algorithms such as EEGNet. The dataset was converted to BIDS format using MOABB and originates from a study investigating the utility of in-ear EEG sensing for motor task classification.
This dataset comprises neurophysiological recordings from 39 participants who received calibrated laser stimuli while rating pain intensity and unpleasantness. Participants were presented with laser stimulations at individually-determined energy levels and provided subjective pain ratings on a 0-10 numeric rating scale. The study investigates the neural mechanisms underlying pain perception and assessment, with potential applications to understanding movement-induced hypoalgesia and related pain modulation phenomena. This is a derivative dataset based on OpenNeuro ds005289.
A preprocessed EEG dataset comprising motor imagery recordings from 14 healthy participants performing imagined right-hand tapping, left-hand tapping, and rest tasks. The dataset contains 1,665 trials acquired at 1024 Hz using a 19-channel montage with standard 10-20 electrode placement. Data have been preprocessed with ICA artifact removal and frequency filtering, and include annotations for motor imagery classification and brain-computer interface applications.
This dataset comprises multimodal physiological recordings from 86 participants during resting state and a digit span working memory task. It includes 64-channel EEG, electrocardiography, photoplethysmography, pupillometry, and behavioral performance data. The dataset enables investigation of neural and peripheral physiological correlates of cognitive load, working memory capacity, and cognitive overload detection across fine temporal scales.
This dataset contains 62-channel EEG recordings from 25 healthy, right-handed subjects performing kinesthetic motor imagery of the right hand versus right elbow, a within-limb discrimination paradigm. Each subject completed 15 sessions over 3 days, yielding 600 trials total, designed to study brain-computer interface (BCI) decoding of different joint movements from the same limb. The dataset supports research into motor rehabilitation and prosthetic control applications using EEG-based BCI systems.
A multi-session EEG dataset acquired from 15 healthy participants performing resting state and graded cognitive tasks (MATB-II at three difficulty levels). The dataset comprises 62-channel EEG recordings at 500 Hz sampling rate designed for passive brain-computer interface applications and mental workload estimation in neuroergonomic contexts. Raw EEG data are provided with standardized event annotations using HED 8.4.0 schema and MOABB-compatible feature extraction pipelines (bandpower analysis and Riemannian covariance methods) for benchmarking purposes.
Imported from OpenNeuro ds004850