Stropahl, Maren and Bauer, Anna-Katharina R. and Debener, Stefan and Bleichner, Martin G. (2018) Source-modeling auditory processes of EEG data using EEGLAB and Brainstorm. Frontiers in neuroscience, 12. p. 309. ISSN 1662-453X

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Official URL: http://dx.doi.org/10.3389/fnins.2018.00309

Abstract

Electroencephalography (EEG) source localization approaches are often used to disentangle the spatial patternsmixed up in scalp EEG recordings. However, approaches differ substantially between experiments, may be strongly parameter-dependent, and results are not necessarily meaningful. In this paper we provide a pipeline for EEG source estimation, from raw EEG data pre-processing using EEGLAB functions up to source-level analysis as implemented in Brainstorm. The pipeline is tested using a data set of 10 individuals performing an auditory attention task. The analysis approach estimates sources of 64-channel EEG data without the prerequisite of individual anatomies or individually digitized sensor positions. First, we show advanced EEG pre-processing using EEGLAB, which includes artifact attenuation using independent component analysis (ICA). ICA is a linear decomposition technique that aims to reveal the underlying statistical sources of mixed signals and is further a powerful tool to attenuate stereotypical artifacts (e.g., eye movements or heartbeat). Data submitted to ICA are pre-processed to facilitate good-quality decompositions. Aiming toward an objective approach on component identification, the semi-automatic CORRMAP algorithm is applied for the identification of components representing prominent and stereotypic artifacts. Second, we present a step-wise approach to estimate active sources of auditory cortex event-related processing, on a single subject level. The presented approach assumes that no individual anatomy is available and therefore the default anatomy ICBM152, as implemented in Brainstorm, is used for all individuals. Individual noise modeling in this dataset is based on the pre-stimulus baseline period. For EEG source modeling we use the OpenMEEG algorithm as the underlying forward model based on the symmetric Boundary Element Method (BEM). We then apply the method of dynamical statistical parametric mapping (dSPM) to obtain physiologically plausible EEG source estimates. Finally, we show how to perform group level analysis in the time domain on anatomically defined regions of interest (auditory scout). The proposed pipeline needs to be tailored to the specific datasets and paradigms. However, the straightforward combination of EEGLAB and Brainstorm analysis tools may be of interest to others performing EEG source localization.

Item Type: Article
Additional Information: Publiziert mit Hilfe des DFG-geförderten Open Access-Publikationsfonds der Carl von Ossietzky Universität Oldenburg.
Uncontrolled Keywords: EEG, source localization, Brainstorm, EEGLAB, auditory N100, auditory processing
Subjects: Philosophy and psychology > Psychology
Science and mathematics > Physics
Technology, medicine, applied sciences > Medicine and health
Divisions: Faculty of Medicine and Health Sciences > Department of Medical Physics and Acoustics
Faculty of Medicine and Health Sciences > Department of Psychology
Date Deposited: 11 Sep 2019 13:57
Last Modified: 25 Sep 2019 08:07
URI: https://oops.uni-oldenburg.de/id/eprint/4148
URN: urn:nbn:de:gbv:715-oops-42298
DOI: doi:10.3389/fnins.2018.00309
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