Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN Autoencoder

Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN Autoencoder

This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, mostly due to muscle artifacts (MA), introduced by the movement of muscles. The e...

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Bibliographic Details
Main Authors: Subham Nagar, Ahlad Kumar
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
EEG signal denoising
convolutional neural networks
autoencoder
Tchebichef moments
compression
Online Access:https://ieeexplore.ieee.org/document/9865981/

Internet

https://ieeexplore.ieee.org/document/9865981/

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