Implicit Learning EEG (BioSemi)
[ recordings of Cortico-Cortical Evoked Potentials (CCEPs) from 74 patients aged 4-51 years, collected during clinical single pulse electrical stimulation (SPES) as part of the RESPect epilepsy surgery patient registry at UMC Utrecht. The data were used to study the developmental trajectory of neural transmission speed across the human lifespan. Electrode positions were localized using Freesurfer with Destrieux atlas labeling and mapped to MNI152 space.
…Imaging Data Structure, http://bids.neuroimaging.io) (Niso et al. 2018) - Niso…
Data collection took place at the Washington University School of Medicine, St…
This high-density EEG dataset comprises recordings from 20 healthy right-handed volunteers (10 females, 10 males, mean age 23 years) collected between 2014 and 2017 during four experimental conditions: resting state, visual naming, auditory naming, and working memory tasks. The dataset contains 256-channel EEG data acquired at 1000 Hz sampling frequency and is designed to investigate dynamic functional brain network reorganization across different cognitive states.
…Cognitive Neuroscience and Neuroimaging An MEG study (306-sensor Elekta Neuromag System…
This dataset comprises high-density electroencephalography (EEG) recordings acquired during a visual stimulation task from human participants. Data were collected in a single session and organized according to the EEG-BIDS specification, with raw recordings stored in BrainVision format. The dataset provides raw, unfiltered EEG signals suitable for subsequent offline preprocessing and analysis of visual evoked responses.
This dataset contains 32-channel EEG recordings from 22 healthy adults performing a visual imagery task involving 10 categories of animals, figures, and objects. Data were collected across two sessions per subject using a Neuracle NeuSenW32 system at 1000 Hz sampling rate, and are formatted according to BIDS for use in brain-computer interface research. The dataset supports evaluation of classification approaches such as CSP and EEGNet for decoding imagined visual categories from EEG signals.
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on004012-blue…