Volume 19, Issue 2 (Summer 2017)                   Advances in Cognitive Science 2017, 19(2): 1-18 | Back to browse issues page

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Rouzbahani M, Motie A. Effective Connectivity Estimation Based on Emotion EEG Signal by Granger Causality and Directed Transfer Function. Advances in Cognitive Science. 2017; 19 (2) :1-18
URL: http://icssjournal.ir/article-1-534-en.html
1- Master of science, Biomedical Engineering, Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University
2- Nasrabadi Associate Professor, Biomedical Engineering, Shahed University
Abstract:   (992 Views)
Introduction: Emotions can be called a complex phenomenon which has been derived from an individual’s daily matters. Emotion has huge effect on individuals’ decision making. Decision making spectrum can have effect on self and social life of a society.
Method: Some pictures with contents of happiness, sadness and neutral have been shown to a group of subjects and a 16-channel EEG signal has been recorded. The goal is to estimate effective connectivity in brain source area. Independent Component Analysis (ICA) is used to move from sensory space to brain source area. The brain sources are sorted and marked based on Shannon entropy. Relation between brain regions will be surveyed by effective connectivity. Granger causality and Direct Transform Function (DTF) is used for estimating effective connectivity.
Results: Based on the result of Granger causality and brain source topographies, different models of effective connectivity have been proposed. Subjects’ individual models were compared with proposed models and each of them were labeled. Accuracy for classification of happiness, sadness and neutral moods are equal to 63.8%, 55.5% and 61.1%.
Conclusion: Proposed model with spatial information based on Granger causality shows that in happy mood brain’s left hemisphere has more activity. In sad mood, the right hemisphere has more activity. In neutral mood, also brain’s left hemisphere is engaged. The occipital and frontal lobes are engaged in information exchange. DTF with respect to Granger causality has less resolution in estimating the connectivity. As a result, proposing model based on it will be harder and less accurate.
Full-Text [PDF 1397 kb]   (561 Downloads)    
Type of Study: Research | Subject: Special
Received: 2016/10/29 | Accepted: 2017/03/5 | Published: 2017/06/22

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