…Pictures were selected and adapted to have similar framing and comparable perceptual…
This dataset contains EEG recordings from 34 participants (one session each) who viewed 242 object images, 121 challenge and 121 control images, selected from comparisons between human behavioural performance and AlexNet. The study examines adaptive recruitment of cortex-wide recurrent processing during visual object recognition. The derivatives folder contains outputs of the decoding analyses, including time-resolved decoding accuracy matrices for object identity (.npz).
EEG recordings from 23 participants performing a passive viewing task with partially occluded and subsequently uncovered scenes containing game pieces on a game board. Conditions varied the number of initially visible pieces (4 or 32) and the number of pieces revealed after the occluder was removed (0 to 32). The dataset supports investigation of neural responses to object numerosity in occluded and uncovered visual scenes.
Continuous 64-channel scalp EEG from 12 observers performing a visual dimension-based attention (cue) task, originally reported by Gramann, Töllner and Müller (2010). The recordings, 56-66 minutes each, include ICA decomposition weights and expert-annotated independent component labels. The dataset was later used to study automatic multi-class labeling of ICA components and is known as the "Cue" dataset.
Stereo-electroencephalography (SEEG) recordings from 140 patients with refractory epilepsy undergoing presurgical evaluation at the University Hospital of Nancy. Participants viewed fast periodic visual stimulation sequences of natural object images at 6 Hz with a face every fifth image (1.2 Hz). The derivative release contains minimally preprocessed, sequence-segmented recordings with Talairach electrode coordinates and anatomical labels, supporting study of face-selective responses across the ventral occipito-temporal cortex.
EEG recordings from participants performing a rapid go/nogo visual task in which briefly flashed (20 ms) natural photographs had to be either categorized (animal vs. non-animal) or recognized (a previously learned probe image). Data were collected over two recording days, in series of 100 images with equally likely target and non-target trials. The dataset supports research on the timing of visual categorization and recognition processes and on single-trial EEG dynamics.
EEG recordings from participants viewing object and word stimuli presented in rapid serial streams at 5 Hz to the left visual field, the right visual field, or centrally. Participants performed an orthogonal red target detection task while stimuli were shown. The dataset supports investigation of the temporal dynamics of visual object coding within and across the cerebral hemispheres, particularly for objects in peripheral vision.
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.
Intracranial EEG and behavioral event data from a paired associates learning task in which participants studied pairs of visually presented words, solved arithmetic distractor problems, and then performed cued recall. Data were collected from clinical sites in collaboration with the Computational Memory Lab at the University of Pennsylvania. Recordings include monopolar and bipolar iEEG with subject-specific electrode montages, MNI/Talairach coordinates, and brain region annotations.
STRUM is a dyadic neuroergonomics dataset in which pairs of participants perform a collaborative urban-reconnaissance task while a time-synchronized multimodal sensor suite records both members simultaneously via Lab Streaming Layer. It covers 33 sessions and 66 participants, with high-density EEG, EMG, ECG/EOG, motion capture, eye-tracking, force plate and controller behavior. Each participant has their own subject directory, and the pairing is encoded in participants.tsv so that analyses can split by pair.