HAD-MEEG
…resolution of magnetoencephalography (MEG) and electroencephalography (EEG). HAD-MEEG were recorded in…
- Participants
- 32
- Channels
- 62 (10-10)
- Size
- 221 GB
- Version
- v1.0.0
- Updated
- Jul 22, 2026
100 results for "electroencephalography" · page 6 of 10 · ranked by relevance
…resolution of magnetoencephalography (MEG) and electroencephalography (EEG). HAD-MEEG were recorded in…
This dataset comprises electroencephalogram (EEG) recordings from 127 young adults (18-30 years) collected during standardized cognitive tasks designed to assess perception, cognition, and action. Participants completed tasks from the Event-Related Potentials Compendium of Open Resources and Experiments (ERP CORE) or adapted variants thereof. The dataset integrates neurophysiological measures with comprehensive socioeconomic indicators spanning childhood and adulthood, including family SES, educational attainment, income, food security, and neighborhood characteristics, as well as ADHD symptom screening. This resource enables investigation of the relationship between socioeconomic context and neural electrophysiology in a diverse young adult population.
This dataset comprises intracranial EEG recordings from 23 patients with drug-resistant epilepsy undergoing stereo-EEG presurgical evaluation. The dataset includes 41 high-frequency stimulation events that evoked negative motor responses, with 24-second iEEG recordings per stimulation event (9-10 seconds pre-stimulation, up to 5 seconds stimulation, and 9-10 seconds post-stimulation). Stimulation parameters, contact pairs, current intensities, and evoked effects are documented in event files, enabling analysis of cortical responses to electrical stimulation.
EmoEEG-MC is a multi-context emotional EEG dataset comprising 64-channel EEG and peripheral physiological recordings from 60 participants exposed to video-induced and imagery-induced emotional stimuli across seven emotion categories (joy, inspiration, tenderness, fear, disgust, sadness, and neutral). This dataset addresses the critical gap in cross-context emotion decoding by enabling investigation of how emotional neural responses generalize across different elicitation contexts, with demonstrated classification accuracies of 66.7% for binary emotion classification and 28.9% for seven-category emotion classification using machine learning approaches.
…EEG-BIDS, an extension to the brain imaging data structure for electroencephalography…
…provided a long-term intracranial electroencephalography (iEEG) dataset of 8 epilepsy patients…
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