TY - JOUR
T1 - EEG Classification of Forearm Movement Imagery Using a Hierarchical Flow Convolutional Neural Network
AU - Jeong, Ji Hoon
AU - Lee, Byeong Hoo
AU - Lee, Dae Hyeok
AU - Yun, Yong Deok
AU - Lee, Seong Whan
N1 - Funding Information:
This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government under Grant 2017-0-00432 (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User’s Thought via AR/VR Interface), in part by the IITP funded by the Korea Government under Grant 2017-0-00451 (Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning), and in part by the IITP funded by the Korea Government under Grant 2019-0-00079 (Department of Artificial Intelligence, Korea University).
PY - 2020
Y1 - 2020
N2 - 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 (±0.04) for the motor execution (ME) task and 0.65 (±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 (±0.03) for ME and 0.51 (±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.
AB - 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 (±0.04) for the motor execution (ME) task and 0.65 (±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 (±0.03) for ME and 0.51 (±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.
KW - Brain-computer interface (BCI)
KW - convolutional neural network (CNN)
KW - electroencephalogram (EEG)
KW - forearm motor execution and motor imagery
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U2 - 10.1109/ACCESS.2020.2983182
DO - 10.1109/ACCESS.2020.2983182
M3 - Article
AN - SCOPUS:85083966924
VL - 8
SP - 66941
EP - 66950
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 9046799
ER -