Published October 23, 2023 | Version v1
Dataset Restricted

Dataset for the article "Blindly separated spontaneous network-level oscillations predict corticospinal excitability"

Creators

  • 1. ROR icon University of Tübingen
  • 2. ROR icon Aalto University
  • 3. ROR icon University of Trento
  • 4. ROR icon University of Toronto

Description

This repository contains a dataset supporting results in the manuscript: Ermolova, M., Metsomaa, J., Belardinelli, P., Zrenner, C., & Ziemann, U. (2024). Blindly separated spontaneous network-level oscillations predict corticospinal excitability. Journal of Neural Engineering, 21(3), 036041.

REFTEP dataset: TMS-EEG experiment on awake healthy human subjects. Single-pulse TMS was applied in resting state over primary motor cortex, with simultaneous EEG recording from the scalp and EMG recording from hand muscles.  

The dataset is intended for use by the code published at: https://github.com/mariaermolova/CSPAnalysis. The dataset is structured as follows: each .mat file corresponds to a single subject and contains a matlab structure with the following substructs. 

1. EEG data from 1.5 sec. before each TMS pulse (eeg). The data was preprocessed: bad trials and channels removed, signals detrended, ICA components corresponding to oculographic artefacts removed. 

2. EEG channel locations on the scalp (chanlocs) and indices of channels removed during preprocessing (removedChannels). 

3. Peak-to-peak amplitudes of Motor Evoked Potentials for each trial (mepSize) and excitability labels for each trial based on the amplitude of the corresponding MEP (labels). Labels correspond to high (1) vs low (0) MEP amplitude.

Files

Restricted

The record is publicly accessible, but files are restricted to users with access.

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  • Please state your name, contact details (e-mail), institution, position, and reason for requesting access to the REFTEP dataset (the dataset presented in the current repository).
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Additional details

Related works

Is supplement to
Journal article: 10.1088/1741-2552/ad5404 (DOI)

Funding

European Commission
Connecting to the Networks of the Human Brain 810377