EEG Classification of Motor Imagery Using a Novel Deep Learning Framework

EEG Classification of Motor Imagery Using a Novel Deep Learning Framework

Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder...

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Bibliographic Details
Main Authors:	Mengxi Dai, Dezhi Zheng, Rui Na, Shuai Wang, Shuailei Zhang
Format:	Article
Language:	English
Published:	MDPI AG 2019-01-01
Series:	Sensors
Subjects:	EEG deep learning short-time Fourier transform convolutional neural network variational autoencoder
Online Access:	https://www.mdpi.com/1424-8220/19/3/551

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