Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_0017) - Open Research Data - Bridge of Knowledge

Your account

login

Search

Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_0017)

Description

Data comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four memory and cognitive tasks lasting approx. 1 hour. Gaze tracking on the task computer screen as well as pupilometry was also recorded together with behavioral performance. The recordings were collected from a minimum of one daily session (run) during a two-week long hospital stay for the seizure monitoring with at least one task from the battery completed. Each dataset comes from one patient and includes metadata about anatomical coordinates of every electrode contact, labels for each electrode contact channel, and timing of events in each task.  All data are stored in BIDS (Brain Imaging Data Structure) format for efficient signal processing supported by the International Neuroinformatics Coordinating Facility), and were collected at Mayo Clinic (USA) or at the Wroclaw Medical University (Poland). These unique datasets arerelevant for anyone interested in neurology, neuroscience and neurophysiology of human memory and cognition.

For more information visit the website of our Brain and Mind Electrophysiology laboratory (brainmindlab.com).
 
Illustration of the publication

Dataset file

ieeg.zip
19.9 GB, S3 ETag b891218a321588793426295518285435-40, downloads: 67
The file hash is calculated from the formula
hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.

Example script for calculation:
https://github.com/antespi/s3md5
download file ieeg.zip

File details

License:
Creative Commons: by 4.0 open in new tab
CC BY
Attribution
File embargo:
2021-08-17
Software:
pymef - Tools for navigating through the dataset. Installation: pip install pybids

Details

Year of publication:
2021
Verification date:
2021-09-22
Dataset language:
English
Fields of science:
  • medical sciences (Medical and Health Sciences )
  • biomedical engineering (Engineering and Technology)
DOI:
DOI ID 10.34808/vebc-sp72 open in new tab
Funding:
Ethical papers:
IRB 10390.007 (Mayo Clinic), KB 564/2019 (Medical University of Wroclaw)
Verified by:
Gdańsk University of Technology

Keywords

References

Cite as

Authors

seen 162 times