Face processing MEEG dataset with HED annotation

OpenNeuro/NEMAR Dataset: ds003645 Files: 1137 Dataset size: 106.4 GB
Channels: 306 MEG,70 EEG,2 EOG,1 ECG,12 Misc,3 Trigger
Participants: 18
Event files: 126 View events summary
HED annotation: Yes View HED tags summary

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README

Introduction: This dataset consists of the MEEG (sMRI+MEG+EEG) portion of the multi-subject, multi-modal face processing dataset (ds000117). This dataset was originally acquired and shared by Daniel Wakeman and Richard Henson (https://pubmed.ncbi.nlm.nih.gov/25977808/). The MEG and EEG data were simultaneously recorded; the sMRI scans were preserved to support M/EEG source localization. Following event log augmentation, reorganization, and HED (v8.0.0) annotation, the EEG data have been repackaged in EEGLAB format.

Overview of the experiment: Eighteen participants completed two recording sessions spaced three months apart – one session recorded fMRI and the other simultaneously recorded MEG and EEG data. During each session, participants performed the same simple perceptual task, responding to presented photographs of famous, unfamiliar, and scrambled faces by pressing one of two keyboard keys to indicate a subjective yes or no decision as to the relative spatial symmetry of the viewed face. Famous faces were feature-matched to unfamiliar faces; half the faces were female. The two sessions (MEEG, fMRI) had different organizations of event timing and presentation because of technological requirements of the respective imaging modalities. Each individual face was presented twice during the session. For half of the presented faces, the second presentation followed immediately after the first. For the other half, the second presentation was delayed by 5-15 face presentations.

Preprocessing: The EEG preprocessing, which was performed using the wh_extracteeg_BIDS.m located in the code directory, includes the following steps:

  • Ignore MRI data except for sMRI.
  • Extract EEG channels out of the MEG/EEG fif data
  • Add fiducials
  • Rename EOG and EKG channels
  • Extract events from event channel
  • Add button press events!
  • Remove spurious event types 5, 6, 7, 13, 14, 15, 17, 18 and 19
  • Remove spurious event types 24 for subject 3 run 4
  • Correct event latencies (events have a shift of 34 ms)
  • Add HED (v8.0.0) event annotations -- see Robbins et al. (2021)
  • Remove event fields urevent and duration
  • Save as EEGLAB .set format

Data curators: Dung Truong, Ramon Martinez, Scott Makeig, Arnaud Delorme (UCSD, La Jolla, CA, USA), Kay Robbins (UTSA, San Antonio, TX, USA)


HED Event descriptors
word cloud:

BIDS Version: 1.8.0 HED Version: 8.1.0 Version: 2.0.2

On Brain life: True Published date: 2021-05-20 17:51:13

Tasks: FacePerception

Available modalities: EEG, MEG, MRI

Format(s): .fdt, .fif, .pos, .set

Sessions: 1 Scans/session: 6 Ages (yrs): 23 - 37 License: CC0

Dataset DOI: doi:10.18112/openneuro.ds003645.v2.0.2

Uploaded by Dung Truong on 2021-05-04 08:12:20

Last Updated 2023-04-14 14:27:58

Authors
Daniel G. Wakeman, Richard N Henson, Dung Truong (curation), Kay Robbins (curation), Scott Makeig (curation), Arno Delorme (curation)

Acknowledgements

How to Acknowledge

Funding
  • Experiment was supported by the UK Medical Research Council (MC_A060_5PR10) and Elekta Ltd.
  • Curation was supported by: Army Research Laboratory W911NF-10-2-0022, NIH R01 EB023297-03, NIH R01 NS047293-l4, and NIH R24 MH120037-01.
  • References and Links
  • Wakeman, D., Henson, R. (2015). A multi-subject, multi-modal human neuroimaging dataset. Sci Data 2, 150001. https://doi.org/10.1038/sdata.2015.1
  • Robbins, K., Truong, D., Appelhoff, S., Delorme, A., & Makeig, S. (2021). Capturing the nature of events and event context using Hierarchical Event Descriptors (HED). In press for NeuroImage Special Issue Practice in MEEG. NeuroImage 245 (2021) 118766. Online: https://www.sciencedirect.com/science/article/pii/S1053811921010387.
  • Robbins, K., Truong, D., Jones, A., Callanan, I., & Makeig, S. (2021). Building FAIR functionality: Annotating events in time series data using Hierarchical Event Descriptors (HED). Neuroinformatics Special Issue Building the NeuroCommons. Neuroinformatics https://doi.org/10.1007/s12021-021-09537-4. Online: https://link.springer.com/article/10.1007/s12021-021-09537-4.
  • Ethics Approvals
  • The study was approved by Cambridge University Psychological Ethics Committee. Written informed consent was obtained from each participant prior to and following each phase of the experiment.