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Open AccessArticle

Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network—Feasibility Study

1
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
2
AI Core Development Team, LG Electronics, Seoul 07796, Korea
3
Center for Biosignals, Korea Research institute of Science and Standards, Daejeon 34113, Korea
4
Department of Medical Physics, University of Science and Technology, Daejeon 34113, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5317; https://doi.org/10.3390/s19235317
Received: 29 October 2019 / Revised: 22 November 2019 / Accepted: 2 December 2019 / Published: 3 December 2019
(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)
Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density EEG systems are necessary for better analysis. Conventional methods have been proposed to solve these problems, however, they depend on parameters or brain models that are not simple to address. Therefore, new approaches are necessary to enhance EEG spatial resolution while maintaining its data properties. In this work, we investigated the super-resolution (SR) technique using deep convolutional neural networks (CNN) with simulated EEG data with white Gaussian and real brain noises, and experimental EEG data obtained during an auditory evoked potential task. SR EEG simulated data with white Gaussian noise or brain noise demonstrated a lower mean squared error and higher correlations with sensor information, and detected sources even more clearly than did low resolution (LR) EEG. In addition, experimental SR data also demonstrated far smaller errors for N1 and P2 components, and yielded reasonable localized sources, while LR data did not. We verified our proposed approach’s feasibility and efficacy, and conclude that it may be possible to explore various brain dynamics even with a small number of sensors. View Full-Text
Keywords: convolutional neural networks; electroencephalography; spatial resolution; super-resolution convolutional neural networks; electroencephalography; spatial resolution; super-resolution
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Kwon, M.; Han, S.; Kim, K.; Jun, S.C. Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network—Feasibility Study. Sensors 2019, 19, 5317.

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