Kojima et al. 2024 (Dataset A) — An auditory brain-computer interface based on selective attention to multiple tone streams
- Participants
- 11
- Size
- 3.74 GB
- Version
- v1.0.1
- Updated
- Jun 2, 2026
Showing 10 of 530 datasets · page 8 of 53
Imported from OpenNeuro ds004362
# Multimodal EEG-fNIRS-physio dataset during hierarchical cognitive-motor tasks This repository contains a raw multimodal dataset acquired in healthy adults performing a hierarchy of cognitive, motor, and combined cognitive-motor tasks. Data include neurophysiological (EEG, fNIRS), physiological (ECG and EMG), behavioral (push-button, torque), and subjective measures (sleepiness and cognitive load ratings), organized according to the Brain Imaging Data Structure (BIDS). The dataset is describe
This dataset contains multimodal physiological recordings acquired during smartphone interaction and video viewing conditions. The dataset includes simultaneous electroencephalography (EEG), eye-tracking, photoplethysmography (PPG), and galvanic skin response (GSR) signals. ## **Experimental Protocol** Participants complete two experimental conditions while wearing a 64-channel EEG cap and a head-mounted eye tracker, with simultaneous PPG and GSR recordings: **Smartphone Monitor condition (10
# PD-EEG: Resting-State & Walking EEG in Parkinson's Disease ## Overview This dataset contains EEG recordings from Parkinson's disease (PD) patients and healthy controls (HC), collected under two behavioral conditions: resting state (sitting) and walking. The dataset was acquired at the Neurology Institute, Tel Aviv Sourasky Medical Center. --- ## Participants - **Parkinson’s disease (PD):** 116 participants - **Healthy controls (HC):** 28 participants ### Inclusion criteria (PD): - Age 40
## Summary This dataset contains magnetoencephalography (MEG) recordings collected while participants read the French text of *Le Petit Prince* presented using a rapid serial visual presentation (RSVP) paradigm. A separate dataset containing MEG recordings from the auditory listening paradigm is available on OpenNeuro (accession number: ds007523). This data is analyzed in: d’Ascoli, S., Bel, C., Rapin, J. et al. Towards decoding individual words from non-invasive brain recordings. Nature Com
## Summary This dataset contains magnetoencephalography (MEG) recordings collected while participants listened to the French audiobook of *Le Petit Prince* by Antoine de Saint-Exupéry. A complementary MEG dataset from the same project, using a reading (RSVP) paradigm, is available on OpenNeuro (accession number: ds007524). This data is analyzed in: d’Ascoli, S., Bel, C., Rapin, J. et al. Towards decoding individual words from non-invasive brain recordings. Nature Communications 16, 10521 (20
A preprint of the manuscript can be found on bioRxiv: doi.org/10.1101/2025.09.09.674354 The experiment and analysis code can be found via the Open Science Framework: doi.org/10.17605/OSF.IO/ZFD7P Experiment Details: Human electroencephalography recordings from 23 participants, who did a letter task and calorie categorisation task. In the letter task, participants viewed rapid streams of overlaid food/non-food images and letters, pressing a button whenever they saw a vowel, while ignoring the i
Imported from OpenNeuro ds002718