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

• Published in: IEEE access Vol. 8; pp. 66941 - 66950
• Main Authors: Jeong, Ji-Hoon, Lee, Byeong-Hoo, Lee, Dae-Hyeok, Yun, Yong-Deok, Lee, Seong-Whan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Brain modeling; Brain-computer interface (BCI); Classification; Convolution; convolutional neural network (CNN); Datasets; Decoding; electroencephalogram (EEG); Electroencephalography; Feature extraction; Forearm; forearm motor execution and motor imagery; Human-computer interface; Image classification; Kinematics; Neural networks; Robot arms; Robot control; Rotation; Task analysis

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, al...