Applying brain emotional learning based fuzzy inference system for EEG signal classication between schizophrenics and control participant

Bahar Javadi Khasraghi, Saeed Setayeshi, Greg Price

Research output: Chapter in Book/Conference paperConference paperpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)9781467389884
DOIs
Publication statusPublished - 4 Oct 2017
Event2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017 - Manchester, United Kingdom
Duration: 23 Aug 201725 Aug 2017

Conference

Conference2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
Country/TerritoryUnited Kingdom
CityManchester
Period23/08/1725/08/17

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