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EEG Classification of Forearm Movement Imagery Using a Hierarchical Flow Convolutional Neural Network

Authors
Jeong, Ji-HoonLee, Byeong-HooLee, Dae-HyeokYun, Yong-DeokLee, Seong-Whan
Issue Date
2020
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Brain-computer interface (BCI); electroencephalogram (EEG); convolutional neural network (CNN); forearm motor execution and motor imagery
Citation
IEEE ACCESS, v.8, pp.66941 - 66950
Indexed
SCIE
SCOPUS
Journal Title
IEEE ACCESS
Volume
8
Start Page
66941
End Page
66950
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/58928
DOI
10.1109/ACCESS.2020.2983182
ISSN
2169-3536
Abstract
Recent advances in brain-computer interface (BCI) techniques have led to increasingly refined interactions between users and external devices. Accurately decoding kinematic information from brain signals is one of the main challenges encountered in the control of human-like robots. In particular, although the forearm of an upper extremity is frequently used in daily life for high-level tasks, only few studies addressed decoding of the forearm movement. In this study, we focus on the classification of forearm movements according to elaborated rotation angles using electroencephalogram (EEG) signals. To this end, we propose a hierarchical flow convolutional neural network (HF-CNN) model for robust classification. We evaluate the proposed model not only with our experimental dataset but also with a public dataset (BNCI Horizon 2020). The grand-average classification accuracies of three rotation angles yield 0.73 (& x00B1;0.04) for the motor execution (ME) task and 0.65 (& x00B1;0.09) for the motor imagery (MI) task across ten subjects in our experimental dataset. Further, in the public dataset, the grand-averaged classification accuracies were 0.52 (& x00B1;0.03) for ME and 0.51 (& x00B1;0.04) for MI tasks across fifteen subjects. Our experimental results demonstrate the possibility of decoding complex kinematics information using EEG signals. This study will contribute to the development of a brain-controlled robotic arm system capable of performing high-level tasks.
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