Empirical Comparison of Distributed Source Localization Methods for Single-Trial Detection of Movement Preparation

Frontiers in Human Neuroscience
Anett SeelandElsa A Kirchner

Abstract

The development of technologies for the treatment of movement disorders, like stroke, is still of particular interest in brain-computer interface (BCI) research. In this context, source localization methods (SLMs), that reconstruct the cerebral origin of brain activity measured outside the head, e.g., via electroencephalography (EEG), can add a valuable insight into the current state and progress of the treatment. However, in BCIs SLMs were often solely considered as advanced signal processing methods that are compared against other methods based on the classification performance alone. Though, this approach does not guarantee physiological meaningful results. We present an empirical comparison of three established distributed SLMs with the aim to use one for single-trial movement prediction. The SLMs wMNE, sLORETA, and dSPM were applied on data acquired from eight subjects performing voluntary arm movements. Besides the classification performance as quality measure, a distance metric was used to asses the physiological plausibility of the methods. For the distance metric, which is usually measured to the source position of maximum activity, we further propose a variant based on clusters that is better suited for the single-tri...Continue Reading

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Citations

Oct 9, 2020·International Journal for Numerical Methods in Biomedical Engineering·Evangelos GalarisConstantinos Siettos

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Methods Mentioned

BETA
feature extraction

Software Mentioned

DBSCAN
FieldTrip
Nutmeg
wMNE
dSPM
learn
Brainstorm
pySPACE
Scikit
sLORETA

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