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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| Published in: | IEEE transactions on emerging topics in computational intelligence Vol. 2; no. 4; pp. 278 - 287 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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IEEE
01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2471-285X, 2471-285X |
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| Abstract | 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 <inline-formula><tex-math notation="LaTeX">\text{15}\%\pm \text{6.3}</tex-math> </inline-formula> (p-value <inline-formula><tex-math notation="LaTeX"><\text{0.05}</tex-math></inline-formula>) and <inline-formula><tex-math notation="LaTeX">\text{22}\%\pm \text{5.7}</tex-math></inline-formula> (p-value <inline-formula><tex-math notation="LaTeX"><\text{0.05}</tex-math></inline-formula>) for subject and event classifications, respectively. |
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| AbstractList | 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. 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 <inline-formula><tex-math notation="LaTeX">\text{15}\%\pm \text{6.3}</tex-math> </inline-formula> (p-value <inline-formula><tex-math notation="LaTeX"><\text{0.05}</tex-math></inline-formula>) and <inline-formula><tex-math notation="LaTeX">\text{22}\%\pm \text{5.7}</tex-math></inline-formula> (p-value <inline-formula><tex-math notation="LaTeX"><\text{0.05}</tex-math></inline-formula>) for subject and event classifications, respectively. |
| Author | Abdelfattah, Sherif Moustafa, Nour Wang, Min Hu, Jiankun |
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| SubjectTerms | 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 |
| Title | Deep Gaussian Mixture-Hidden Markov Model for Classification of EEG Signals |
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