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

Min Wang, Sherif Abdelfattah, Nour Moustafa, Jiankun Hu

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)


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 models. In this paper, we propose a novel method that can automatically capture the nonstationary dynamics of EEG signals for diverse classification tasks. It consists of two components. The first component uses an autoregressive-deep variational autoencoder model for automatic feature extraction, and the second component uses a Gaussian mixture-hidden Markov model for EEG classification with the extracted features. We compare the performance of our proposed method and the state-of-the-art methods in two EEG classification tasks, subject, and event classification. Results show that our approach outperforms the others by averages of 15% ± 6.3 (p-value < 0.05) and 22% ± 5.7 (p-value < 0.05) for subject and event classifications, respectively.

Original languageEnglish
Pages (from-to)278-287
Number of pages10
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Issue number4
Publication statusPublished - Aug 2018
Externally publishedYes


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