Applying brain emotional learning based fuzzy inference system for EEG signal classication between schizophrenics and control participant
This paper concerns the diagnosis of schizophrenia using encephalographic signals and introduces a new framework based on image processing technique. Time-frequency approach or spectrogram image processing technique was used to analyze EEG signals. The spectrogram images were formed from EEG signals...
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| Veröffentlicht in: | 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) S. 1 - 8 |
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01.08.2017
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| Abstract | This paper concerns the diagnosis of schizophrenia using encephalographic signals and introduces a new framework based on image processing technique. Time-frequency approach or spectrogram image processing technique was used to analyze EEG signals. The spectrogram images were formed from EEG signals, then the Gray Level Co-occurrence Matrix (GLCM) texture feature was extracted from the images. This texture feature produced huge matrix data, thus we used locally linear embedding algorithm (LLA) to reduce the big matrix. In this model, the neuro-based computational model on the limbic system was used to discriminate subjects with schizophrenia patients and control participant that models the emotional process. This architecture is a merging algorithm based on brain emotional learning and fuzzy inference system. The results showed that the proposed model is able to classify the electroencephalographic spectrogram image with 81.5% accuracy. |
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| AbstractList | This paper concerns the diagnosis of schizophrenia using encephalographic signals and introduces a new framework based on image processing technique. Time-frequency approach or spectrogram image processing technique was used to analyze EEG signals. The spectrogram images were formed from EEG signals, then the Gray Level Co-occurrence Matrix (GLCM) texture feature was extracted from the images. This texture feature produced huge matrix data, thus we used locally linear embedding algorithm (LLA) to reduce the big matrix. In this model, the neuro-based computational model on the limbic system was used to discriminate subjects with schizophrenia patients and control participant that models the emotional process. This architecture is a merging algorithm based on brain emotional learning and fuzzy inference system. The results showed that the proposed model is able to classify the electroencephalographic spectrogram image with 81.5% accuracy. |
| Author | Setayeshi, Saeed Khasraghi, Bahar Javadi Price, Greg |
| Author_xml | – sequence: 1 givenname: Bahar Javadi surname: Khasraghi fullname: Khasraghi, Bahar Javadi email: bahar.javadi.ai@gmail.com organization: Department of Computer Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran – sequence: 2 givenname: Saeed surname: Setayeshi fullname: Setayeshi, Saeed email: setayesh@aut.ac.ir organization: Department of Energy Engineering and Physics, Amirkabir University, Tehran, Iran – sequence: 3 givenname: Greg surname: Price fullname: Price, Greg email: Greg.Price@health.wa.gov.au organization: University of Western Australia, Graylands Hospital, Perth, Australia |
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| Snippet | This paper concerns the diagnosis of schizophrenia using encephalographic signals and introduces a new framework based on image processing technique.... |
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| SubjectTerms | Adaptation models Amygdala Analytical models Biological neural networks Brain modeling Computational modeling Electroencephalography Emotional learning Fuzzy Orbitofrontal Schizophrenics Spectrogram |
| Title | Applying brain emotional learning based fuzzy inference system for EEG signal classication between schizophrenics and control participant |
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