100 results for "Inter-brain synchrony" · page 8 of 10 · ranked by relevance
…Target=2, NonTarget=1 Trial interval: [0, 1.0] s Acquisition ----------- Sampling…
This dataset comprises resting-state EEG recordings and behavioral executive function assessments from 140 healthy adult participants, collected to investigate the relationship between intrinsic brain spatio-temporal dynamics (via EEG microstates) and executive functions. Participants underwent a 5-minute resting-state EEG recording (eyes open/closed) followed by a battery of nine cognitive tasks assessing executive functions. The study aimed to correlate EEG microstate metrics (occurrence, duration) with executive function performance scores.
…Network Healthy Brain Network (HBN) Project, curated into the Brain Imaging Data…
…brain-computer interface, BCI, event-related potentials, ERP, P300, learning from label…
Imported from OpenNeuro ds006126
This dataset comprises intracranial EEG recordings and single-unit neuronal activity from the human amygdala of nine epilepsy patients during exposure to dynamic visual stimuli with aversive (fearful faces) and neutral (landscape) content. The recordings enable investigation of amygdalar responses to emotional stimuli across multiple spatial scales, from macroscopic field potentials to microscopic neuronal firing patterns. Data are provided in BIDS format with extended neuronal spike data available in NIX standard.
…Personalized whole-brain activity patterns predict human corticospinal tract activation in real…
This dataset contains neuroimaging recordings from 29 participants investigating human brain responses to nociceptive stimulation. The study examines neural activity patterns during concomitant stimulation of Aδ and C nociceptors, contributing to understanding of pain processing mechanisms in the human brain.
…event-related potential (ERP)** brain–computer interface (BCI) designed for scalable data…
This dataset comprises EEG recordings from 84 healthy participants performing motor imagery tasks across multiple paradigms (Graz motor imagery, SSMVEP-MI, and hybrid video/SSVideo paradigms) in two distinct recording environments: a controlled electromagnetically shielded laboratory and a simulated multi-sensory hospital setting. A supplementary dataset from 3 participants recorded in a space station environment is also included, extending the study to microgravity conditions. The dataset is intended to support research on cross-environment robustness, cross-subject decoding, and benchmarking of EEG-based brain-computer interface algorithms.