Showing 10 of 572 datasets · page 17 of 58
PREDICT is an EEG dataset containing recordings from participants with chronic pain conditions. The dataset includes resting-state and task-based EEG recordings along with clinical and demographic information. This dataset is mirrored on NEMAR from OpenNeuro (ds005486) and is intended to support research on predictive biomarkers and neural mechanisms of chronic pain.
STReEF is a long-term intracranial EEG dataset from 13 patients with focal epilepsy, comprising 5 electrocorticography and 8 stereo-encephalography recordings collected at the University Medical Center Utrecht. The dataset includes structural and functional neuroimaging data organized according to BIDS specification, designed to investigate structural and effective brain connectivity in focal epilepsy surgery candidates.
This dataset comprises raw EEG recordings from 15 participants performing three auditory oddball paradigms: Optimum-1 (optimized for mismatch negativity), learning-oddball (optimized for P3b), and local–global paradigm (for local and global effects). The data were collected in a single EEG session with three consecutive tasks presented as separate sessions, enabling within-individual comparison of event-related potential sensitivity across specialized oddball sequences.
This dataset comprises EEG recordings from 24 electrodes acquired during a mixed reality stereo vision task designed to characterize mental fatigue. Participants completed resting and movement observation scenes within a virtual environment, with EEG data collected to enable both resting-state and task-state analyses of fatigue-related neural correlates.
This dataset comprises stereoelectroencephalography (sEEG) recordings from epilepsy patients hospitalized on a monitoring unit at Oregon Health & Science University, acquired during presentation of symbolic (Arabic numerals and spoken words) and non-symbolic (dot patterns and auditory tones) numerical stimuli. The study investigates neural responses to numerical processing across different sensory modalities in patients with epilepsy. Note: Some anatomical derivatives and processing files from FreeSurfer surface reconstruction are included as part of the analysis pipeline.
This dataset comprises behavioral and intracranial electrophysiological recordings from a repeated-item free recall task conducted across multiple clinical sites. Participants studied lists of 12 unique words (with varying repetition frequencies: 3 presented once, 3 twice, and 6 three times) presented visually, followed by a 7-second delay and free recall phase. The dataset investigates how item repetition affects memory encoding and retrieval processes through direct brain recordings.
This dataset contains neuroimaging data from a semantic conditioning task designed to investigate how contingency awareness modulates neural responses during fear conditioning. The study examines the relationship between explicit awareness of stimulus-outcome contingencies and the neural mechanisms underlying conditioned fear responses, contributing to understanding of conscious versus unconscious learning processes in aversive conditioning.
This dataset comprises auditory brainstem response (ABR) recordings from 25 normal-hearing participants exposed to continuous speech under varying masking conditions. Participants listened to peaky speech from one to five simultaneous talkers at different signal-to-noise ratios (clean, 0 dB, -3 dB, -6 dB SNR), as well as click trains, while EEG was recorded. The study investigates how speech masking affects subcortical neural encoding of naturally produced speech in human listeners.
This dataset comprises EEG recordings investigating the neural mechanisms of action observation and integration. Using frequency tagging methodology, the study examines how the brain processes and integrates dissimilar observed actions, providing insights into the neural basis of action perception and motor cognition.
This dataset comprises electroencephalography (EEG), electroglottography (EGG), and audio recordings collected during a delayed auditory feedback experiment. Participants performed speech tasks while receiving real-time auditory feedback of their own speech with experimentally controlled delays. The dataset includes minimally preprocessed EGG data with hardware filter phase delay corrections and synchronized audio recordings representing the delayed feedback heard by subjects.