on002680
Go-nogo categorization and detection task
EEG
…a categorization task and a recognition task. In both tasks, target images…
- Participants
- 14
- Channels
- 31 (10-20)
- Size
- 9.22 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
Discover datasets
22 results for "categorization" · page 1 of 3 · ranked by relevance
-
- on004809
Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories
BEH iEEGThis dataset comprises behavioral events and intracranial electrophysiological recordings from a categorized free recall task conducted across multiple clinical sites. Participants studied semantically organized word lists (12 items from 3 categories with paired exemplars), performed a distractor task, and freely recalled the words. The dataset includes monopolar and bipolar iEEG recordings with electrode localization information, supporting investigations of memory encoding and retrieval processes.
- Participants
- 258
- Citations
- 1
- Size
- 477 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on004995
The Time-Course of Food Representation in the Human Brain
EEG[
BEH iEEGThis dataset comprises intracranial EEG recordings and behavioral events from a categorized free recall task with closed-loop electrical stimulation applied during encoding. Participants studied word lists organized by semantic category, performed arithmetic distractor tasks, and freely recalled words, while a classifier trained on prior encoding data determined stimulation delivery based on predicted memory encoding success. Data were collected across multiple clinical sites in collaboration with the Computational Memory Lab at the University of Pennsylvania, as part of the DARPA Restoring Active Memory (RAM) project.
- Participants
- 9
- Size
- 12.2 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- on007012
FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images
BEH EEGFOODEEG is an open dataset of electroencephalographic (EEG) and behavioural responses to food images from 117 participants across two testing sessions. Session 1 involved a food categorisation task with continuous EEG recording, while Session 2 comprised a food go/no-go task and a food paired choice task, along with questionnaires on dietary style and eating motivations. The dataset also includes normative ratings on 22 food attributes collected from an independent online sample (N = 624), providing a rich resource for studying neural and behavioural correlates of food-related decision-making.
- Participants
- 117
- Size
- 91.1 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- on007964
Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding
EEGThis dataset contains 128-channel EEG recordings from 20 observers (19 included in final analysis) viewing object images at 3.33 Hz to investigate how contextual associations, perceptual attributes, and conceptual properties of objects are represented in neural activity. One participant was excluded due to a technical error in EEG recording. Time-resolved neural decoding was applied to disentangle these distinct representational dimensions from the EEG signals.
- Participants
- 20
- Citations
- 2
- Size
- 18.9 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000232
THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition
EEGTHINGS-EEG2 is a large-scale EEG dataset comprising recordings from 10 subjects viewing 16,540 distinct training images and 200 test images presented via rapid serial visual presentation at 5 Hz. The dataset includes 63-channel EEG data sampled at 1000 Hz across 4 sessions per subject, with approximately 32,540 training trials and 16,000 test trials, designed to support computational modeling of human visual object recognition. Stimuli are drawn from the THINGS database, and the dataset includes resting-state recordings and behavioral annotations for each trial.
- Participants
- 10
- Channels
- 63 (10-10)
- Citations
- 82
- Size
- 198 GB
- Version
- v1.1.0
- Updated
- Jul 10, 2026
- nm000182
Multicenter iEEG dataset for classification of graphoelements and artifactual signals (MAYO)
iEEGA multicenter intracranial electroencephalography (iEEG) dataset comprising segmented 3-second single-channel clips from epilepsy patients, annotated for graphoelement classification and artifact detection. The dataset includes 10,000+ clips from multiple subjects with preserved clinical metadata including seizure onset zone (SOZ) flags, electrode anatomy, and reviewer annotations. This resource supports the development and validation of automated signal processing algorithms for clinical neurophysiology applications.
- Participants
- 24
- Size
- 15.0 GB
- Version
- v1.0.2
- Updated
- Jul 10, 2026