A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory

• Published in: Behavioral and brain functions Vol. 14; no. 1; p. 17
• Main Authors: Zangeneh Soroush, Morteza, Maghooli, Keivan, Setarehdan, Seyed Kamaledin, Nasrabadi, Ali Motie
• Format: Journal Article
• Language: English
• Published: England BioMed Central 31.10.2018; BMC
• Subjects: Adult; Brain - physiology; Brain computer interactions; Brain research; Classification; Databases, Factual; Dempster Shafer theory; EEG; Electrocardiography; Electroencephalography; Electroencephalography - methods; Embedded systems; Emotion identification; Emotions; Emotions - physiology; Female; Humans; Independent component analysis; International conferences; Local subset feature selection; Machine Learning; Machine learning methods; Male; Methods; Music - psychology; Neurosciences; Physiology; Recognition (Psychology) - physiology; Video Recording - methods; Wavelet transforms; Young Adult; Local subset feature selection; Brain computer interactions; Emotion identification; Machine learning methods; Dempster Shafer theory; Independent component analysis
• ISSN: 1744-9081; 1744-9081
• DOI: 10.1186/s12993-018-0149-4

Emotion recognition is an increasingly important field of research in brain computer interactions. With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature. The current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence. The presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced. The present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.