EEG Classification of Forearm Movement Imagery Using a Hierarchical Flow Convolutional Neural Network
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...
Main Authors: | Ji-Hoon Jeong, Byeong-Hoo Lee, Dae-Hyeok Lee, Yong-Deok Yun, Seong-Whan Lee |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9046799/ |
Similar Items
-
SessionNet: Feature Similarity-Based Weighted Ensemble Learning for Motor Imagery Classification
by: Byeong-Hoo Lee, et al.
Published: (2020-01-01) -
Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique
by: Ridha Djemal, et al.
Published: (2016-08-01) -
Motor Imagery EEG Classification Using Capsule Networks
by: Kwon-Woo Ha, et al.
Published: (2019-06-01) -
Multi-class motor imagery EEG decoding for brain-computer interfaces
by: Deng eWang, et al.
Published: (2012-10-01) -
A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification
by: Hao Wu, et al.
Published: (2019-11-01)