16 results for "PhysioBank" · page 1 of 2 · ranked by relevance
…Goldberger, A. et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a…
BIDS-EMG dataset - HDsEMG recordings of tibialis anterior during isometric contractions at 30% and 50% MVC, 6 subjects, 256-channel grids (MUniverse benchmark)
A high-density scalp electroencephalogram (HD-EEG) dataset acquired during sensorimotor rhythm-based brain-computer interface (BCI) tasks. This dataset comprises recordings from multiple subjects performing motor imagery and related sensorimotor tasks, designed to support the development and validation of BCI systems. The HD-EEG recordings provide detailed spatial resolution of cortical activity relevant to motor control and brain-computer interfacing research.
…R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a…
This dataset comprises high-density scalp EEG recordings collected during sensorimotor rhythm-based brain-computer interface (BCI) tasks. It was designed to support research into motor imagery decoding and neurofeedback training using EEG signals. The dataset accompanies a data descriptor published in Scientific Data, providing detailed EEG recordings for BCI-related sensorimotor rhythm analysis.
…R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a…
A high-density scalp electroencephalogram (EEG) dataset acquired during sensorimotor rhythm-based brain-computer interface (BCI) tasks. This dataset comprises recordings from multiple subjects performing motor imagery and related sensorimotor activities, designed to support the development and validation of BCI systems. The dataset provides a comprehensive resource for investigating neural correlates of motor planning and brain-computer communication.
…Database `<https://archive.physionet.org/physiobank/database/mssvepdb/>`_ S. Nikolopoulos, 2016, DataAcquisitionDetails…
This dataset comprises high-density scalp electroencephalography (EEG) recordings collected during sensorimotor rhythm-based brain-computer interface (BCI) tasks. It is intended to support research on motor imagery decoding, neural correlates of sensorimotor rhythms, and BCI performance modeling. The dataset represents a derivative or related version of a previously published high-density EEG BCI dataset.
…Database `<https://archive.physionet.org/physiobank/database/mssvepdb/>`_ S. Nikolopoulos, 2016, DataAcquisitionDetails…