Journal
ENTROPY
Volume 21, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/e21121199
Keywords
brain-computer interface (BCI); electroencephalography (EEG); motor imagery (MI); continuous wavelet transform (CWT); convolutional neural network (CNN)
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Funding
- Kwangwoon University
- Institute for Information & communications Technology Promotion (IITP) - Korean Government (MISP) [2018-0-00735]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2018-0-00735-002] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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