TX15 is an EEG dataset organized according to BIDS 1.8.0 with HED annotations (version 8.1.0). It is a NEMAR-hosted version of the OpenNeuro dataset ds004842 and is released under a CC0 license. The README provides minimal detail on the experimental paradigm or participants.
…Donders Center for Cognition - **Country**: NL - **Repository**: GitHub - **Data URL**: https://public…
EEG dataset from 21 participants performing a continuous control task in which they steered a ship, shown as a rotating arrow, toward a target using left and right key presses. The underlying dynamics were unstable, based on the pole-and-cart problem. Participants were cued before each round about the level of controller noise and environmental noise (wind), giving four noise conditions. Event triggers mark cues, round start, button presses, crashes, successes, and points display.
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EEG dataset from twelve participants who completed three reinforcement learning tasks that differed in average task value. In each task, participants learned cue-response mappings for six coloured-shape cues, using left or right key presses. Cues had feedback validity of 0.5 (low-value) or 0.8 (high-value), and the tasks contained only low-value cues, only high-value cues, or a mix of both. The dataset supports investigation of how task-level value affects trial-level reward processing.
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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.
EEG recordings from 54 healthy participants performing a 36-symbol ERP (P300) row-column speller task with face stimuli, collected over two sessions with offline training and online copy-spelling test phases. Data were recorded at 1000 Hz from 62 EEG channels plus 4 additional EMG channels. The dataset is part of a three-paradigm BCI collection (ERP, SSVEP, motor imagery) created to investigate BCI illiteracy and distributed with the OpenBMI toolbox.
EEG dataset from 19 participants performing a visual P300 speller-like paradigm to assess how visual stimulus properties affect evoked responses and classification accuracy. Each participant completed three sessions with conditions varying the number of stimuli (4 to 9) and the flash type (standard flash versus a superimposed yellow smiling cartoon face). Recordings comprise 382 files from eight electrodes sampled at 256 Hz, with markers encoding trial phases and activated symbols.