Showing 10 of 581 datasets · page 16 of 59
This dataset contains EEG recordings from a color change detection task, organized according to the Brain Imaging Data Structure (BIDS) standard. It is intended to support research on visual attention and perceptual change detection processes.
This dataset comprises repeated-awakening EEG recordings collected during propofol sedation, designed to explore dream experiences and consciousness dynamics under anesthesia. EEG data were acquired across wakefulness (eyes open/closed) and multiple sedation and awakening conditions, including sessions with transcranial magnetic stimulation. The study aims to evaluate whether EEG complexity measures can capture subjective dream reports elicited through repeated awakenings during propofol-induced sedation.
This dataset contains EEG recordings from an alphabetic decision task using stimuli presented in the Arial Light font, collected to investigate sub-letter orthographic processing. It provides raw electrophysiological and behavioural data intended to support modeling of visual word recognition using optimal transport approaches. The dataset was collected at Goethe University and is derived from a related source dataset.
This dataset contains EEG recordings from 23 participants performing a passive viewing task involving partially occluded and uncovered visual scenes containing varying numbers of game pieces. The study investigates how the brain processes numerosity information under conditions of occlusion and disocclusion. Participants viewed scenes with 4 or 32 initially visible game pieces, followed by uncovering to reveal different numbers of additional pieces, across 640 trials.
This dataset comprises intracranial electrocorticography (ECoG) recordings from 9 epilepsy patients implanted with grid, depth, and strip electrodes (1,330 electrodes total), collected while participants listened to a 30-minute naturalistic story containing over 5,000 words. It provides raw and minimally preprocessed (high-gamma band) neural data along with aligned auditory stimuli, word-level transcripts, and linguistic features spanning low-level acoustics to large language model embeddings. The dataset is intended to support research on natural language comprehension using high-fidelity invasive recordings and includes tutorials replicating prior findings.
This dataset contains EEG recordings from a study examining expectation effects on conflict processing, in which participants performed a task involving conflict stimuli under varying levels of expectation. The data support investigations into the neural mechanisms underlying cognitive control and expectation-driven modulation of conflict-related brain activity. This dataset is mirrored on NEMAR from OpenNeuro (ds005571).
This dataset contains EEG recordings from an ERP priming study investigating neural associations between fingerspelling, print, and signs in deaf readers. Participants viewed prime-target pairs consisting of English words, ASL signs, or fingerspelled words followed by English word targets, allowing examination of cross-modal semantic priming effects. The study was conducted at San Diego State University's NeuroCognition Laboratory.
This 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.
This dataset contains behavioral events and intracranial electrophysiology recordings from a delayed free recall task with closed-loop electrical stimulation delivered during encoding, guided by a classifier trained on encoding data. Participants studied lists of visually presented words, performed arithmetic distractor tasks, and then freely recalled the words. Data were collected at multiple clinical sites in collaboration with the Computational Memory Lab at the University of Pennsylvania, and represent a closed-loop stimulation extension of the FR1 and FR2 free recall datasets.
The Bitbrain Open Access Sleep (BOAS) dataset comprises 128 nights of simultaneous polysomnography (PSG) and wearable EEG headband recordings from 108 unique participants, designed to validate wearable EEG technology against clinical PSG for sleep monitoring. The dataset includes human-consensus sleep stage labels derived from expert scoring of PSG recordings, as well as AI-generated sleep stage labels for both PSG and wearable EEG data using a deep learning model. Additional demographic metadata (age, sex, BMI) are provided for each participant, offering a comprehensive resource for sleep staging research and wearable EEG validation.