A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks

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ISSN Print: 2153-1285
ISSN Online: 2153-1293
www.scirp.org/journal/cs
E-mail: cs@scirp.org

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"A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks"
written by Harikumar Rajaguru, Sunil Kumar Prabhakar,
published by Circuits and Systems, Vol.7 No.8, 2016

has been cited by the following article(s):

[1]	Deep Learning Algorithms in EEG Signal Decoding Application: A Review
	IEEE …, 2021

[2]	深度学习算法在脑电信号解码中的应用
	2019

[3]	Interpretation of Autoencoders and PCA with Adaboost Classifier for Classification of Epilepsy from EEG Signals
	2018

[4]	Wavelet Transform Analysis (Haar and Sym8) for Epilepsy Classification with Soft Discriminant Classifier
	2017

[5]	Hilbert transform with elman backpropagation and multilayer perceptrons for epilepsy classification
	2017

[6]	A SIMPLIFIED AND EFFICIENT EPILEPSY CLASSIFICATION TECHNIQUE FROM EEG SIGNALS USING PCA
	2006

[1]	Deep Learning Algorithms in EEG Signal Decoding Application: A Review IEEE Access, 2021 DOI:10.1109/ACCESS.2021.3105917

[2]	Interpretation of Autoencoders and PCA with Adaboost Classifier for Classification of Epilepsy from EEG Signals 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), 2018 DOI:10.1109/ICIRCA.2018.8597337

[3]	Hilbert transform with Elman backpropagation and multilayer perceptrons for epilepsy classification 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), 2017 DOI:10.1109/ICECA.2017.8203601

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