A Deep Transfer Convolutional Neural Network Framework for EEG Signal Classification

A Deep Transfer Convolutional Neural Network Framework for EEG Signal Classification

Nowadays, motor imagery (MI) electroencephalogram (EEG) signal classification has become a hotspot in the research field of brain computer interface (BCI). More recently, deep learning has emerged as a promising technique to automatically extract features of raw MI EEG signals and then classify them...

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Bibliographic Details
Main Authors: Gaowei Xu, Xiaoang Shen, Sirui Chen, Yongshuo Zong, Canyang Zhang, Hongyang Yue, Min Liu, Fei Chen, Wenliang Che
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Motor imagery (MI)
electroencephalogram (EEG)
signal classification
short time Fourier transform (STFT)
VGG-16
transfer learning
Online Access:https://ieeexplore.ieee.org/document/8772136/

Internet

https://ieeexplore.ieee.org/document/8772136/

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