Removal of muscular artefacts for the analysis of brain oscillations: Comparison between ICA and SSD

Abstract : The electroencephalogram (EEG) is contaminated by undesired signals of non-neural origin, such as movements of the eyes and muscles. The most common approach for muscle artefact reduction is the linear transformation of EEG signals into source components using Blind Source Separation (BSS) techniques, to separate artefac-tual and neuronal sources. Here we present a case study in which we are interested in clean oscillatory EEG activity. We compare the frequently used Independent Component Analysis (ICA) approach with the recently proposed spatio-spectral decomposition (SSD) method. SSD is designed to extract components that explain oscillations-related variance, and is several orders of magnitude faster than ICA. We investigate EEG data from 18 subjects performing self-paced foot movements with respect to event-related desynchroni-sation (ERD) in the beta band. Results indicate that SSD recovers cleaner signals than ICA on this data set.
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Communication dans un congrès
ICML Workshop on Statistics, Machine Learning and Neuroscience (Stamlins 2015), Jul 2015, Lille, France. 2015
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  • HAL Id : hal-01225250, version 1

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Irene Winkler, Stefan Haufe, Klaus-Robert Mueller. Removal of muscular artefacts for the analysis of brain oscillations: Comparison between ICA and SSD. ICML Workshop on Statistics, Machine Learning and Neuroscience (Stamlins 2015), Jul 2015, Lille, France. 2015. 〈hal-01225250〉

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