SEREEGA: Simulating event-related EEG activity

Krol, LR; Pawlitzki, J; Lotte, F; Gramann, K; Zander, TO

SEREEGA: Simulating event-related EEG activity

Krol, LR Pawlitzki, J Lotte, F Gramann, K

Zander, TO

Permalink

Publication Type:: Journal Article
Citation:: Journal of Neuroscience Methods, 2018, 309 pp. 13 - 24
Issue Date:: 2018-11-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted Manuscript VersionAdobe PDF (3.27 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Krol, LR	en_US
dc.contributor.author	Pawlitzki, J	en_US
dc.contributor.author	Lotte, F	en_US
dc.contributor.author	Gramann, K https://orcid.org/0000-0003-2673-1832	en_US
dc.contributor.author	Zander, TO	en_US
dc.date.available	2020-05-25T19:06:08Z
dc.date.issued	2018-11-01	en_US
dc.identifier.citation	Journal of Neuroscience Methods, 2018, 309 pp. 13 - 24	en_US
dc.identifier.issn	0165-0270	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130158
dc.description.abstract	© 2018 Elsevier B.V. Background: Electroencephalography (EEG) is a popular method to monitor brain activity, but it is difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings. Simulated data can be used, among other things, to assess or compare signal processing and machine learning algorithms, to model EEG variabilities, and to design source reconstruction methods. New method: We present SEREEGA, Simulating Event-Related EEG Activity. SEREEGA is a free and open-source MATLAB-based toolbox dedicated to the generation of simulated epochs of EEG data. It is modular and extensible, at initial release supporting five different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general workflow of this toolbox, as well as a simulated data set demonstrating some of its functions. The toolbox is available at https://github.com/lrkrol/SEREEGA. Results: The simulated data allows established analysis pipelines and classification methods to be applied and is capable of producing realistic results. Comparison with existing methods: Most simulated EEG is coded from scratch. The few open-source methods in existence focus on specific applications or signal types, such as connectivity. SEREEGA unifies the majority of past simulation methods reported in the literature into one toolbox. Conclusion: SEREEGA is a general-purpose toolbox to simulate ground-truth EEG data.	en_US
dc.relation.ispartof	Journal of Neuroscience Methods	en_US
dc.relation.isbasedon	10.1016/j.jneumeth.2018.08.001	en_US
dc.subject.classification	Neurology & Neurosurgery	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Sound Spectrography	en_US
dc.subject.mesh	Evoked Potentials	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Software	en_US
dc.title	SEREEGA: Simulating event-related EEG activity	en_US
dc.type	Journal Article
utslib.citation.volume	309	en_US
utslib.for	1109 Neurosciences	en_US
utslib.for	1701 Psychology	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	309	en_US

Abstract:

© 2018 Elsevier B.V. Background: Electroencephalography (EEG) is a popular method to monitor brain activity, but it is difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings. Simulated data can be used, among other things, to assess or compare signal processing and machine learning algorithms, to model EEG variabilities, and to design source reconstruction methods. New method: We present SEREEGA, Simulating Event-Related EEG Activity. SEREEGA is a free and open-source MATLAB-based toolbox dedicated to the generation of simulated epochs of EEG data. It is modular and extensible, at initial release supporting five different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general workflow of this toolbox, as well as a simulated data set demonstrating some of its functions. The toolbox is available at https://github.com/lrkrol/SEREEGA. Results: The simulated data allows established analysis pipelines and classification methods to be applied and is capable of producing realistic results. Comparison with existing methods: Most simulated EEG is coded from scratch. The few open-source methods in existence focus on specific applications or signal types, such as connectivity. SEREEGA unifies the majority of past simulation methods reported in the literature into one toolbox. Conclusion: SEREEGA is a general-purpose toolbox to simulate ground-truth EEG data.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/130158