Showing 10 of 575 datasets · page 6 of 58
This dataset comprises intracranial EEG recordings acquired during non-REM sleep from patients undergoing clinical monitoring. The data were collected to investigate high-frequency activity patterns and develop comprehensive models of neural oscillations during sleep, contributing to understanding of intracranial electrophysiology and sleep neuroscience.
This dataset comprises intracranial EEG recordings acquired during non-REM sleep from patients undergoing clinical monitoring. The data were collected to investigate high-frequency activity patterns and develop comprehensive models of neural oscillations during sleep, as described in Hatano et al. (in press). This represents part 3 of a multi-part iEEG high-frequency activity modeling study.
This dataset comprises intracranial EEG recordings acquired during non-REM sleep from multiple subjects. The data were collected to develop and validate a comprehensive model of high-frequency activity (HFA) in epilepsy research. This represents part 1 of a multi-part dataset designed to characterize neural oscillations and their clinical significance during sleep states.
SUBEMOcondEEG1 is a neurophysiological dataset comprising EEG, pupillometry, and behavioral data from 28 healthy participants (final sample after quality control from 32 initial volunteers; 4 excluded due to low signal-to-noise ratio on electrophysiological data) engaged in a fear conditioning paradigm with auditory stimuli. The study investigates emotional learning through three phases (Pre-Conditioning, Conditioning, and Extinction) using voices with varying acoustic properties (high and low amplitude modulation) paired or unpaired with aversive white noise. This dataset provides multimodal measurements of physiological and behavioral responses during fear acquisition and extinction.
A long-term intracranial electroencephalography (iEEG) dataset from 8 epilepsy patients implanted with responsive neurostimulation (RNS) devices, comprising bilateral recordings from epileptic lesion areas. Each recording captures 90 seconds of dual-channel iEEG centered on stimulation events, with detailed stimulation parameters and seizure event markers. The dataset enables investigation of seizure onset pattern evolution and iEEG characteristic changes following electrical stimulation therapy.
This EEG dataset investigates how attended information is maintained in working memory despite varying cognitive load expectations. Participants performed a color-recall task with two memory screens presenting studied and novel objects under conditions of high or low anticipated load. The study examines neural correlates of working memory maintenance and the effects of load expectation on memory-related brain activity, using event-related potentials and spectral analysis of EEG signals.
This dataset contains raw and processed EEG recordings from participants engaged in silent visual reading of word stimuli presented in naturalistic narrative contexts. The study investigates auditory representations of words during reading by recording neural activity while participants view unique and repeated story narratives. The dataset includes BrainVision format EEG files, event annotations with stimulus triggers, preprocessed signals organized by channel, and corresponding feature embeddings for word-level analysis.
This dataset contains electrophysiological recordings from the auditory cortex of macaque monkeys, acquired using multi-channel electrode arrays. The data represents raw neural activity and provides a valuable resource for investigating the neural mechanisms underlying auditory cortical organization in non-human primates. This is a NEMAR mirror of the OpenNeuro dataset ds007028.
This dataset comprises de-identified resting-state EEG recordings from individuals with Parkinson's disease and age-matched healthy controls, collected to evaluate whether EEG biomarkers can distinguish subjects with differential mortality outcomes. Recordings were acquired using standard clinical EEG systems at 500 Hz sampling rate with 10–20 electrode montage during eyes-open rest conditions. The dataset includes mortality classification labels (living/deceased) and is structured according to BIDS EEG standards, enabling investigation of EEG correlates of mortality risk and development of machine learning classification models.
FOODEEG is an open dataset comprising electroencephalographic (EEG) and behavioral responses from 117 participants to food images. The dataset includes raw and processed EEG recordings from a food categorization task, along with behavioral data from food go/no-go and paired choice tasks. Participants also provided continuous ratings on food attributes and completed questionnaires assessing dietary styles and eating motivations, with normative ratings collected from an additional 624 online participants.