Volume 14, Issue 5 (September & October-In-Press 2023)                   BCN 2023, 14(5): 0-0 | Back to browse issues page

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Khajeh Hosseini M S, Pourmir Firoozabadi M, Badie K, Azad Fallah P. EEG Emotion Classification Using a Novel Adaptive Ensemble Classifier Considering Personality Traits. BCN 2023; 14 (5)
URL: http://bcn.iums.ac.ir/article-1-2360-en.html
EEG Emotion Classification Using a Novel Adaptive Ensemble Classifier Considering Personality Traits
Mohammad Saleh Khajeh Hosseini1 , Mohammad Pourmir Firoozabadi * 2, Kambiz Badie3 , Parviz Azad Fallah4
1- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2- Department of Medical Physics, Tarbiat Modares, Tehran, Iran.
3- University of Tehran & ITRC, Tehran, Iran.
4- Department of Psychology, Tarbiat Modares, Tehran, Iran.
Abstract:  
Electroencephalograph (EEG) signals reveal much of human brain states and this method can widely use in emotion classification. Although, the classification of emotion recognition is not almost ideal mainly due to the following reasons: (i) the features extracted from EEG signals may not solely reflect emotional patterns of a person and is affected by some time-varying factor and noise; and (ii) higher-level cognitive factor such as personality, mood, past experiences, etc.  The dynamic properties of EEG data in relation to time series may affect the variability of feature distribution and interclass discrimination at different time stages. In this paper, we suggest a new adaptive ensemble classification method to alleviate the problems mentioned above. Specifically, we propose a new method for providing emotional stimuli. The Stimuli were sorted incrementally based on their valence- arousal score in three groups (sadness, neutral, and happiness).60 subjects 19–30 years of age (mean 25.01 and SD 3.13) participated in this study. The results show that the performance of emotion classifiers in this study has significantly improved compared to conventional classifiers. The classification accuracy elicited by the proposed method is 87.96 %.
Keywords: Adaptive ensemble learning, EEG emotion classification, Personality traits
Full-Text [PDF 1198 kb]      
Type of Study: Original | Subject: Cognitive Neuroscience
Received: 2021/11/26 | Accepted: 2022/04/12 | Published: 2023/09/29
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