Skip to content

Eye movement and blink-related EEG and MEG artifact correction algorithms

License

Notifications You must be signed in to change notification settings

rkobler/eyeartifactcorrection

Repository files navigation

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').

Releases

No releases published

Packages

No packages published

Languages