EEG based emotion recognition using minimum spanning tree
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SCIENTIFIC PAPER
EEG based emotion recognition using minimum spanning tree Sajjad Farashi1,2 · Reza Khosrowabadi3 Received: 12 November 2019 / Accepted: 29 June 2020 © Australasian College of Physical Scientists and Engineers in Medicine 2020
Abstract Emotion is a fundamental factor that influences human cognition, motivation, decision making and social interactions. This psychological state arises spontaneously and goes with physiological changes that can be recognized by computational methods. In this study, changes in minimum spanning tree (MST) structure of brain functional connectome were used for emotion classification based on EEG data and the obtained results were employed for interpretation about the most informative frequency content of emotional states. For estimation of interaction between different brain regions, several connectivity metrics were applied and interactions were calculated in different frequency bands. Subsequently, the MST graph was extracted from the functional connectivity matrix and its features were used for emotion recognition. The results showed that the accuracy of the proposed method for separating emotions with different arousal levels was 88.28%, while for different valence levels it was 81.25%. Interestingly, the system performance for binary classification of emotions based on quadrants of arousal-valence space was also higher than 80%. The MST approach allowed us to study the change of brain complexity and dynamics in various emotional states. This capability provided us enough knowledge to claim lower-alpha and gamma bands contain the main information for discrimination of emotional states. Keywords Emotion recognition · Minimum spanning tree · Graph theory · Electroencephalography · Arousal-valence space · Pattern recognition
Introduction Automatic emotion recognition by means of computerized algorithms has gained special attention during the last decade. Emotion recognition is necessary for developing brainmachine or brain-computer interfaces for disabled people who have no ability to express their emotions using voice or facial expression [1]. In addition, in neuromarketing understanding customer interest in specific products using emotion recognition techniques is an attractive issue [2, 3]. Moreover, emotion recognition has been used in many medical and Electronic supplementary material The online version of this article (https://doi.org/10.1007/s13246-020-00895-y) contains supplementary material, which is available to authorized users. * Sajjad Farashi [email protected] 1
Hamadan University of Medical Sciences, Hamadan, Iran
2
Autism Spectrum Disorder Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
3
Institute for Cognitive and Brain Sciences, Shahid Beheshti University GC, Tehran, Iran
social researches such as studies on depression [4], Parkinson’s disease [5] and autism spectrum disorder [6]. Overall, investigation of brain mechanisms involved in the process of emotional stimuli and using automatic techniques t
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