Skip to content

Latest commit

 

History

History
41 lines (27 loc) · 3.59 KB

README.md

File metadata and controls

41 lines (27 loc) · 3.59 KB
Raw

Eye movement and blink-related artifact correction in EEG and MEG data

This repository contains reference implementations of

  • the sparse generalized eye artifact subspace subtraction (SGEYESUB) algorithm presented in [1].
  • 4 other eye artifact correction algorithms presented in [2-5].

After the algorithm parameters are fitted to calibration data, eye movement and blink-related eye artifacts can be corrected offline and online. In [1,2] calibration data was recorded using a visually guided paradigm. A reference implementation using Psychtoolbox and labstreaminglayer is provided in the subfolder paradigm.

This repository comes also with a demonstration dataset containing electroencephalographic (EEG) and electrooculographic (EOG) activity of one person. The data is stored in eeglab format.

Public EEG dataset

The pre-processed EEG dataset investigated in [1] is publicly available on OSF [6].

Getting started

  • Download or clone the repository
  • Startup the eeglab toolbox
  • Open the demo_main.m script. The script loads a calibration dataset (demo_trainset.set) and an evaluation dataset (demo_testset.set).
  • Both demo datasets contain continuous recordings. Before the algorithms are fitted and evaluated, the datasets are pre-processed in the script demo_preprocessing.m
  • The detailed pre-processing steps are presented in [1].
  • Next an object of the algorithm is created with algo = sgeyesub()
  • The object is fitted to the calibration data algo.fit(X_trn, y_trn, eeg_chan_idxs) where X_trn and y_trn contain the M/EEG (and EOG) signals and labels.
  • New samples (data) are corrected with x_corrected = algo.apply(x).

References

[1] Kobler, R. J., Sburlea, A. I., Lopes-Dias, C., Schwarz, A., Hirata, M. and Müller-Putz, G. R. "Corneo-retinal-dipole and eyelid-related eye artifacts can be corrected offline and online in electroencephalographic and magnetoencephalographic signals.", 218 (2020). https://doi.org/10.1016/j.neuroimage.2020.117000

[2] Kobler, R. J., Sburlea, A. I., and Müller-Putz G.R., "A Comparison of Ocular Artifact Removal Methods for Block Design Based Electroencephalography Experiments." In Proceedings of the 7th Graz Brain-Computer Interface Conference, 236–41, 2017. https://doi.org/10.3217/978-3-85125-533-1-44

[3] Schlögl, A., Keinrath, C., Zimmermann, D., Scherer, R., Leeb, R., and Pfurtscheller, R. "A Fully Automated Correction Method of EOG Artifacts in EEG Recordings." Clinical Neurophysiology 118, no. 1 (2007): 98–104. https://doi.org/10.1016/j.clinph.2006.09.003

[4] Plöchl, M., Ossandón, J. P., and König P. "Combining EEG and Eye Tracking: Identification, Characterization, and Correction of Eye Movement Artifacts in Electroencephalographic Data." Frontiers in Human Neuroscience 6, (2012): 1–23. https://doi.org/10.3389/fnhum.2012.00278

[5] Zhou, X., Gerson, A. D., Lucas C Parra, L. C., and Paul Sajda, P. "EEGLAB Plugin EYESUBTRACT," (2005). Retrieved from http://sccn.ucsd.edu/eeglab/plugins/eyesubtract1.0.zip

[6] Kobler, R. J., Sburlea, A. I., Lopes-Dias, C., Schwarz, A., Mondini, V., and Müller-Putz, G. R. "EEG eye artifact dataset." (2020) Retrieved from https://doi.org/10.17605/OSF.IO/2QGRD

Acknowledgements

This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Consolidator Grant 681231 'Feel Your Reach').