NDLI: Perturbation-evoked potentials can be classified from single-trial EEG

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Perturbation-evoked potentials can be classified from single-trial EEG

Content Provider	Scilit
Author	Ditz, Jonas C. Schwarz, Andreas Müller-Putz, Gernot R.
Copyright Year	2020
Description	Journal: Journal of Neural Engineering Objective. Loss of balance control can have serious consequences on interaction between humans and machines as well as the general well-being of humans. Perceived balance perturbations are always accompanied by a specific cortical activation, the so-called perturbation-evoked potential (PEP). In this study, we investigate the possibility to classify PEPs from ongoing EEG. Approach. Fifteen healthy subjects were exposed to seated whole-body perturbations. Each participant performed 120 trials; they were rapidly tilted to the right and left, 60 times respectively. Main results. We achieved classification accuracies of more than 85% between PEPs and rest EEG using a window-based classification approach. Different window lengths and electrode layouts were compared. We were able to achieve excellent classification performance (87.6 ± 8.0% accuracy) by using a short window length of 200 ms and a minimal electrode layout consisting of only the Cz electrode. The peak classification accuracy coincides in time with the strongest component of PEPs, called N1. Significance. We showed that PEPs can be discriminated against ongoing EEG with high accuracy. These findings can contribute to the development of a system that can detect balance perturbations online.
Related Links	https://iopscience.iop.org/article/10.1088/1741-2552/ab89fb/pdf
ISSN	17412560
e-ISSN	17412552
DOI	10.1088/1741-2552/ab89fb
Journal	Journal of Neural Engineering
Issue Number	3
Volume Number	17
Language	English
Publisher	IOP Publishing
Publisher Date	2020-04-16
Access Restriction	Open
Subject Keyword	Journal: Journal of Neural Engineering Human-machine Interaction Passive Brain-computer Interface Perturbation-evoked Potential
Content Type	Text
Resource Type	Article
Subject	Cellular and Molecular Neuroscience Biomedical Engineering

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