Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural network

• URL: http://arxiv.org/abs/2002.00210v2
• Date: Tue, 4 Feb 2020 04:48:44 GMT
• Title: Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural network
• Authors: Byeong-Hoo Lee, Ji-Hoon Jeong, Kyung-Hwan Shim, Seong-Whan Lee
• Abstract summary: We propose an end-to-end role assigned convolutional neural network (ERA-CNN) which considers discriminative features of each upper limb region. We demonstrate the possibility of decoding user intention by using only EEG signals with robust performance using ERA-CNN.
• Score: 21.984302611206537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Electroencephalogram (EEG) motor imagery (MI) paradigm is widely used in non-invasive BCI to obtain encoded signals contained user intention of movement execution. However, EEG has intricate and non-stationary properties resulting in insufficient decoding performance. By imagining numerous movements of a single-arm, decoding performance can be improved without artificial command matching. In this study, we collected intuitive EEG data contained the nine different types of movements of a single-arm from 9 subjects. We propose an end-to-end role assigned convolutional neural network (ERA-CNN) which considers discriminative features of each upper limb region by adopting the principle of a hierarchical CNN architecture. The proposed model outperforms previous methods on 3-class, 5-class and two different types of 7-class classification tasks. Hence, we demonstrate the possibility of decoding user intention by using only EEG signals with robust performance using an ERA-CNN.

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