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Open AccessArticle

Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface

Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea
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Entropy 2019, 21(12), 1199; https://doi.org/10.3390/e21121199
Received: 8 November 2019 / Revised: 3 December 2019 / Accepted: 3 December 2019 / Published: 5 December 2019
(This article belongs to the Special Issue Entropy in Image Analysis II)
The motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has been receiving attention from neural engineering researchers and is being applied to various rehabilitation applications. However, the performance degradation caused by motor imagery EEG with very low single-to-noise ratio faces several application issues with the use of a BCI system. In this paper, we propose a novel motor imagery classification scheme based on the continuous wavelet transform and the convolutional neural network. Continuous wavelet transform with three mother wavelets is used to capture a highly informative EEG image by combining time-frequency and electrode location. A convolutional neural network is then designed to both classify motor imagery tasks and reduce computation complexity. The proposed method was validated using two public BCI datasets, BCI competition IV dataset 2b and BCI competition II dataset III. The proposed methods were found to achieve improved classification performance compared with the existing methods, thus showcasing the feasibility of motor imagery BCI. View Full-Text
Keywords: brain-computer interface (BCI); electroencephalography (EEG); motor imagery (MI); continuous wavelet transform (CWT); convolutional neural network (CNN) brain-computer interface (BCI); electroencephalography (EEG); motor imagery (MI); continuous wavelet transform (CWT); convolutional neural network (CNN)
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Lee, H.K.; Choi, Y.-S. Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface. Entropy 2019, 21, 1199.

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