…2022, DANDI 000397) ====================================================================================== Overview -------- Recordings from a single Neuropixels 1.0 silicon…
A mobile EEG motor imagery dataset from two pilots, one tetraplegic and one able-bodied, recorded during development of the neuroTUM brain-computer interface for the 2024 Cybathlon BCI race. Data were acquired with a 24-channel wireless Smarting mobi system at 250 Hz using an arrow-cue paradigm with subject-specific sets of three mental tasks (rest, hand and/or foot motor imagery). The dataset is re-released in BIDS format with HED annotations via MOABB.
This dataset contains simultaneous 64-channel EEG and fMRI recordings from 16 participants performing right-hand motor imagery with bimodal EEG-fMRI neurofeedback (experiment XP2). Participants were randomly assigned to receive either a mono-dimensional or a bi-dimensional feedback display. It includes raw and preprocessed EEG, BOLD fMRI, and EEG and BOLD neurofeedback scores, supporting multi-modal data integration research. It is designed to be combined with the companion XP1 dataset.
This dataset contains laser-evoked potentials recorded from the human spinal cord and cortex. It combines electrophysiological recordings to characterize nociceptive responses along the neuraxis. The data are organized in EEG-BIDS format and shared under a CC0 license.
A multi-modal neuroimaging dataset (XP1) with simultaneous 64-channel EEG and fMRI recorded while 10 participants performed right-hand kinaesthetic motor imagery with neurofeedback. Each participant completed motor localizer, pre- and post-training motor imagery runs, and three neurofeedback runs (EEG-only, fMRI-only, and bimodal EEG-fMRI). The release includes raw and preprocessed EEG, BOLD fMRI, anatomical data, and EEG and BOLD neurofeedback scores to support multi-modal data integration.
EEG recordings from nine end-users with central nervous system tissue damage (stroke or spinal cord injury) performing five cue-guided mental tasks: word association, mental subtraction, spatial navigation, right-hand motor imagery, and feet motor imagery. Data were recorded with 30 active EEG electrodes across two sessions per participant, on different days. The dataset supports brain-computer interface research on individually adapted imagery and binary classification of mental tasks.
This dataset contains EEG recordings from 9 healthy subjects performing cued motor imagery of left hand, right hand, feet, and tongue movements, known as BCI Competition IV Dataset 2a. Each subject completed two sessions of six runs, with 22 EEG and 3 EOG channels sampled at 250 Hz. It is widely used as a benchmark for multi-class motor imagery classification in brain-computer interface research.
This dataset provides simultaneous EEG and fNIRS recordings from 12 participants performing a silent naming task and three sensory-based imagery tasks (visual, auditory, tactile) on objects from the semantic categories of animals and tools. It supports research on semantic decoding and brain-computer interfacing. Stimulus images and example Python scripts for loading the data with MNE and MNE-BIDS are included.
This dataset, named DataSet2, is an EEG dataset originally deposited on OpenNeuro (ds002833) and mirrored on NEMAR. It was contributed by Mheich and colleagues. The available documentation gives no details on the experimental paradigm or participants.
This dataset contains simultaneously recorded EEG and fMRI data from 22 adults aged 23 to 51 years, collected at the Nathan Kline Institute while participants watched naturalistic video stimuli. It also includes eye tracking, physiological (respiration) recordings, demographic information, and behavioral measures such as sleep and caffeine intake. The goal is to investigate the relationship between electrical brain activity and hemodynamic fluctuations.