A Dynamic Multi-Scale Network for EEG Signal Classification

A Dynamic Multi-Scale Network for EEG Signal Classification

Accurate and automatic classification of the speech imagery electroencephalography (EEG) signals from a Brain-Computer Interface (BCI) system is highly demanded in clinical diagnosis. The key factor in designing an automatic classification system is to extract essential features from the original in...

Full description

Bibliographic Details
Main Authors: Guokai Zhang, Jihao Luo, Letong Han, Zhuyin Lu, Rong Hua, Jianqing Chen, Wenliang Che
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-01-01
Series:Frontiers in Neuroscience
Subjects:
brain-computer interface
electroencephalography
multi-scale
Fourier transform
dynamic learning
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2020.578255/full

Internet

https://www.frontiersin.org/articles/10.3389/fnins.2020.578255/full

Similar Items