InsurgentCivilian is an EEG dataset with HED (Hierarchical Event Descriptors) annotations, contributed by Johnson, Gordon, Touryan, and King. The README describes it only as a placeholder, so detailed information on the task, participants, and acquisition is not provided.
Scalp video-EEG recordings from 14 adults with epilepsy, acquired at the Unit of Neurology and Neurophysiology of the University of Siena, Italy. The dataset comprises about 128 hours of recording at 512 Hz using the 10-20 system, with ECG channels and 47 annotated seizures. It is a BIDS curation of the PhysioNet Siena Scalp EEG Database, collected for developing patient-specific seizure prediction and monitoring devices.
NEMAR Dataset nm000105: discrete_gestures - Discrete hand gesture detection from EMG
VEPCON is a multimodal BIDS dataset with high-density EEG, structural MRI and diffusion-weighted imaging from 20 participants. Visual evoked potentials were recorded during face versus scrambled-face discrimination and coherent versus incoherent motion discrimination. Derivatives include preprocessed single-trial EEG, FreeSurfer outputs, multi-scale brain parcellations (83 to 1015 regions), structural connectomes, and EEG source-imaging inverse solutions, supporting multimodal methods development and structure-function studies.
Stereo-EEG recordings from six patients with medically refractory epilepsy performing a scene recognition memory task with concurrent EyeLink eye tracking. The dataset accompanies a study of how the human hippocampus rapidly coordinates effective learning, and is a lossless iEEG-BIDS re-packaging of the authors' public Zenodo release. It includes electrode MNI coordinates, behavioural arrays, eye-tracking files and the authors' analysis code in sourcedata.
A multimodal dataset of simultaneous 64-channel EEG, head-mounted eye-tracking, photoplethysmography (PPG), and galvanic skin response (GSR) recordings from 23 healthy adults. Participants took part in a naturalistic smartphone use condition and a standardized video viewing condition. Hardware TTL pulses synchronize the EEG and eye-tracking streams for temporal alignment across modalities.
A visual event-related potential dataset of 124-channel EEG recorded from 10 healthy participants viewing photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, inanimate object). It contains 5,184 trials, with 72 images per category shown for 500 ms with a 750 ms interstimulus interval. The data support research on neural object representations through single-trial EEG classification and representational similarity analysis, and are distributed in BIDS format via MOABB.
EEG dataset from an instruction-following study in which participants received novel verbal instructions combining integration or selection of animate and inanimate images, a relevant feature (color or shape), and a response key. Each participant completed a main instruction-following task and a 1-back localizer task in a single session. The dataset supports investigation of neural representations of novel instructions.
THINGS-EEG2 is a large EEG dataset from 10 participants who viewed 16,540 training images and 200 repeatedly presented test images from the THINGS database. Images were shown in rapid serial visual presentation at 5 Hz while 63-channel EEG was recorded at 1000 Hz across four sessions per subject. The dataset supports modeling of human visual object recognition and includes resting-state recordings, the authors' preprocessed epochs, and stimulus images.
This dataset contains multimodal recordings from an n-back cognitive workload task with four difficulty levels (1-back to 4-back). It includes 19-channel mobile EEG, ECG, and Pupil Labs eye-tracking and pupillometry data, synchronized with Lab Streaming Layer. Twenty participants were recorded and 18 are released, with two excluded for data quality failures. The data were converted from XDF to BIDS.