100 results for "wrist-based EMG" · page 8 of 10 · ranked by relevance
…cue-based Mode: online Training/test split: False Instructions: Subjects received auditory…
This dataset contains EEG recordings from 10 healthy, right-handed participants performing simple and compound limb motor imagery tasks, including imagined movements of the left hand, right hand, both hands, feet, and combined hand-foot movements, as well as rest periods. The data were collected using a 60-channel EEG system (plus EOG channels) at Tianjin University to study EEG oscillatory patterns and cognitive processes underlying motor imagery, and have been converted to BIDS format with HED event annotations by the MOABB project.
…layer electroencephalography (EEG), neck electromyography (EMG), inertial measurement unit (IMU) acceleration, ground…
Imported from OpenNeuro ds004362
…For EMG, a bipolar electrode configuration was positioned on the left eyebrow…
This dataset contains intracranial EEG (iEEG) recordings from human patients, rodents, pigs, and non-human primates acquired using novel flexible, scalable, high-resolution micro-stereo-electrodes. The study evaluates the performance of these custom-fabricated depth electrodes for neural recording and includes direct electrical stimulation experiments to assess stimulation effects on brain activity. Raw and preprocessed epoched data are provided in iEEG-BIDS format with accompanying channel maps and derivatives.
…I also collected Corrugator EMG (may be labeled EKG) and Skin Conductance…
This dataset comprises EEG and hand kinematics data recorded during an intention-action conflict paradigm, in which participants performed hand movements while cognitive intention and executed action were experimentally manipulated. The data are intended to support research into the neural and behavioral correlates of intention-action conflict. Data were organized into BIDS format for public sharing and analysis.
…least one EEG and one EMG channel. The number of EEG channels…
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.