59 results for "pupillography" · page 1 of 6 · ranked by relevance
[ at the 2nd Department of Neurology, AHEPA University Hospital, Thessaloniki, Greece, using a 19-channel clinical EEG system. The dataset includes both raw and preprocessed (denoised) EEG data, intended to support research on neurodegenerative disease biomarkers and cognitive decline.
A motor imagery brain-computer interface dataset comprising EEG recordings from 30 healthy participants performing left-hand grasping imagery and rest tasks, augmented with pupillometry measurements. The dataset includes 32-channel EEG data sampled at 512 Hz acquired using BioSemi ActiveTwo hardware, with two experimental runs per subject containing 50 trials total. This derivative dataset was processed using the Mother of All BCI Benchmarks (MOABB) framework and is designed to support BCI research and algorithm development.
This dataset comprises 64-channel EEG recordings from 7 participants performing an auditory imagery task, in which they imagined sounds associated with animals and tools following visual stimulus cues. The dataset is intended to support research on semantic decoding of imagined auditory content and brain-computer interface applications.
Imported from OpenNeuro ds002718
This dataset comprises simultaneous EEG-fMRI recordings from 22 participants (ages 23-51) during naturalistic viewing, collected at the Nathan Kline Institute. The 64-channel EEG data (61 cortical channels, 2 EOG, 1 ECG) was acquired concurrently with fMRI, supplemented by eye-tracking and respiratory recordings. The dataset includes demographic information and behavioral assessments (sleep quality, caffeine intake) to investigate correlations between electrical brain activity and hemodynamic fluctuations during naturalistic stimulation.
Imported from OpenNeuro ds002578
This dataset provides a BIDS-standardized version of simultaneously collected EEG and eye-tracking data from one subject, derived from the larger EEGEyeNet dataset. It supports research on eye movement prediction from electroencephalography and eye-tracking signals, following an established paradigm designed for benchmarking EEG-based eye movement decoding.
This dataset comprises multimodal physiological recordings from 23 healthy adult participants collected during naturalistic smartphone interaction and standardized video viewing conditions. Simultaneous EEG (64-channel), eye-tracking, photoplethysmography (PPG), and galvanic skin response (GSR) data were acquired, synchronized via hardware TTL pulses. The dataset supports research into physiological and neural correlates of smartphone use and passive video viewing.