Motor Imagery Classification of Single-Arm Tasks Using Convolutional
Neural Network based on Feature Refining
- URL: http://arxiv.org/abs/2002.01122v1
- Date: Tue, 4 Feb 2020 04:36:09 GMT
- Title: Motor Imagery Classification of Single-Arm Tasks Using Convolutional
Neural Network based on Feature Refining
- Authors: Byeong-Hoo Lee, Ji-Hoon Jeong, Kyung-Hwan Shim, Dong-Joo Kim
- Abstract summary: Motor imagery (MI) is commonly used for recovery or rehabilitation of motor functions due to its signal origin.
In this study, we proposed a band-power feature refining convolutional neural network (BFR-CNN) to achieve high classification accuracy.
- Score: 5.620334754517149
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Brain-computer interface (BCI) decodes brain signals to understand user
intention and status. Because of its simple and safe data acquisition process,
electroencephalogram (EEG) is commonly used in non-invasive BCI. One of EEG
paradigms, motor imagery (MI) is commonly used for recovery or rehabilitation
of motor functions due to its signal origin. However, the EEG signals are an
oscillatory and non-stationary signal that makes it difficult to collect and
classify MI accurately. In this study, we proposed a band-power feature
refining convolutional neural network (BFR-CNN) which is composed of two
convolution blocks to achieve high classification accuracy. We collected EEG
signals to create MI dataset contained the movement imagination of a
single-arm. The proposed model outperforms conventional approaches in 4-class
MI tasks classification. Hence, we demonstrate that the decoding of user
intention is possible by using only EEG signals with robust performance using
BFR-CNN.
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