This dataset comprises EEG recordings organized in BIDS format, collected from participants at pre- and post-intervention time points. It is intended to support analyses of intervention-related changes in brain activity across two experimental groups.
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EEG recordings from 20 healthy participants who wrote ten letters (a, d, e, f, j, n, o, s, t, v) with the right index finger after a visual cue. The dataset supports classification of handwritten characters, either directly from low-frequency and broadband EEG or with a two-step approach that first decodes continuous hand kinematics and then classifies the letter. It is intended for research on non-invasive brain-computer interfaces for communication.
An EEG dataset from the NeuroTechs group, collected in the context of STEM (science, technology, engineering and mathematics) skills. Recordings were acquired with a Unicorn wearable EEG system through its Python API, and values are provided in microvolts. The dataset is organized according to BIDS with HED annotations.
This dataset contains behavioral events and intracranial EEG recordings from a delayed free recall task in which participants studied visually presented word lists, solved arithmetic distractor problems, and then freely recalled the words. Data were collected at clinical sites in collaboration with the Computational Memory Lab at the University of Pennsylvania. It is a preliminary cognitive electrophysiology study that precedes the FR1 and CatFR1 datasets.
Study P of the bigP3BCI collection, an open, machine-learning-ready P300-based brain-computer interface dataset. It contains 32-channel EEG recorded at 256 Hz from 19 participants with ALS, each over two sessions, while they used a 9x8 character grid speller with predictive and non-predictive spelling paradigms. Target and non-target flash events are annotated with HED tags, and the data are reformatted from the original PhysioNet release into BIDS.
This dataset contains EEG recordings organized in BIDS format, comparing groups measured before and after an intervention. It is intended to support analyses of pre- and post-intervention neural activity changes.
This dataset contains longitudinal resting-state EEG recordings from 43 healthy adults across four sessions spanning about two years, with an average inter-session interval of 7.2 months. It was collected to evaluate the test-retest reliability of EEG measures processed with an automated pipeline combining independent component analysis and wavelet-enhanced artifact removal. The data support analyses of neural components, relative power in regions of interest, and the longitudinal stability of quantitative EEG.
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.
EEG recordings from 30 healthy participants performing a left-hand grasping motor imagery task versus rest, cued by auditory stimuli, with simultaneous pupillometry. The dataset was recorded with a 32-channel BioSemi ActiveTwo system at 512 Hz across two runs of 25 trials per subject. It supports evaluating whether task-evoked pupil diameter changes can improve EEG-based motor imagery brain-computer interface classification.