Deep Gaussian Mixture-Hidden Markov Model for Classification of EEG Signals

Electroencephalography (EEG) signals are complex dynamic phenomena that exhibit nonlinear and nonstationary behaviors. These characteristics tend to undermine the reliability of existing hand-crafted EEG features that ignore time-varying information and impair the performances of classification mode...

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Bibliographic Details
Published in:	IEEE transactions on emerging topics in computational intelligence Vol. 2; no. 4; pp. 278 - 287
Main Authors:	Wang, Min, Abdelfattah, Sherif, Moustafa, Nour, Hu, Jiankun
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	autoencoder Autoregressive models Brain modeling Classification deep learning EEG classification Electroencephalography Feature extraction Gaussian mixture model hidden Markov model Hidden Markov models Indexing Machine learning Markov analysis Markov chains Markov processes Signal classification Task analysis time-series
ISSN:	2471-285X, 2471-285X
Online Access:	Get full text
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