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Main Title: SEREEGA: Simulating event-related EEG activity
Author(s): Krol, Laurens R.
Pawlitzki, Juliane
Lotte, Fabien
Gramann, Klaus
Zander, Thorsten O.
Type: Article
Abstract: 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 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.
Subject(s): electroencephalography
brain–computer interface
ground truth
Issue Date: 14-Aug-2018
Date Available: 6-Jan-2021
Is Part Of: 10.14279/depositonce-10656
Language Code: en
DDC Class: 153 Kognitive Prozesse, Intelligenz
004 Datenverarbeitung; Informatik
Sponsor/Funder: EC/H2020/714567/EU/Boosting Brain-Computer Communication with high Quality User Training/BrainConquest
Journal Title: Journal of Neuroscience Methods
Publisher: Elsevier
Volume: 309
Publisher DOI: 10.1016/j.jneumeth.2018.08.001
Page Start: 13
Page End: 24
EISSN: 1872-678X
ISSN: 0165-0270
TU Affiliation(s): Fak. 5 Verkehrs- und Maschinensysteme » Inst. Psychologie und Arbeitswissenschaft » FG Biopsychologie und Neuroergonomie
Appears in Collections:Technische Universität Berlin » Publications

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