DataSet1 is an EEG dataset originally deposited on OpenNeuro (ds002791) and mirrored on NEMAR. It was contributed by Mheich, Dufor, Yassine, Kabbara, Wendling and Hassan. The available documentation gives no further detail on the experimental paradigm or participants.
Scalp EEG recordings from 27 stroke patients performing a lower-limb motor imagery task under a multi-paradigm protocol with longitudinal, repeated-session training. The dataset supports research on motor-imagery brain-computer interfaces for lower-limb and gait rehabilitation after stroke. It is distributed in BIDS format and was collected at Tianjin University.
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.
This dataset contains intracranial recordings acquired in humans using the CorTec BrainInterchange implantable device together with BCI2000 software. It supports the development of an ecosystem of technology and protocols for adaptive neuromodulation research. The data are shared in BIDS format with iEEG recordings.
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 64-channel EEG recordings and behavioural ratings from four participants who read and evaluated 210 short English-language texts: Haiku, Senryu, and non-poetic control texts. Each text was rated on aesthetic appeal, vivid imagery, being moved, originality, and creativity. These participants' recordings were acquired in segments because of session interruptions and were concatenated during preprocessing. They are released as a companion to the main Poetry Assessment EEG Dataset 1 for completeness and transparency.
NOD-EEG is an electroencephalography dataset of human responses to naturalistic ImageNet object images. It extends the Natural Object Dataset-fMRI (NOD-fMRI) by recording EEG, and MEG in a companion dataset, from the same subjects viewing the same stimuli. Together the three modalities support study of object recognition with both high spatial and high temporal resolution. The dataset includes raw BIDS data, preprocessed continuous data, epoched data, and detailed trial-level metadata.
Intraoperative recordings from a single Neuropixels 1.0 silicon probe (384 sites) inserted into human cortex during neurosurgery at Massachusetts General Hospital. Three participants were recorded without a task, in prefrontal cortex during DBS lead implantation and in anterior temporal lobe during lobectomy. This release contains only the local-field-potential band (0.5-500 Hz, 2.5 kHz), losslessly converted from the DANDI NWB files into iEEG-BIDS (BrainVision) format.
This dataset contains simultaneously recorded scalp EEG and intracranial EEG (depth electrodes) from fifteen epilepsy patients undergoing intracranial monitoring while performing a modified Sternberg verbal working memory task. Encoding, maintenance, and recall are temporally separated. It includes electrode MNI coordinates with anatomical labels, trial-level behavioral information, and LCMV beamforming-reconstructed virtual sensor data for several cortical regions. The data support studies of working memory, connectivity, and replay of memory items.
A longitudinal motor imagery EEG dataset from 18 BCI-naive participants recorded across six sessions (one offline, five online) with 22 EEG channels at 512 Hz. It supports the study of transfer learning for brain-computer interface skill acquisition, comparing unsupervised Generic Recentering and supervised Personally Assisted Recentering for calibration-free training. Participants performed left/right hand motor imagery with continuous visual feedback.