An Adaptive Approach of Fused Feature Extraction for Emotion Recognition Using EEG Signals
Emotion recognition is a basic part towards complete correspondence among human and machine. Recently, research efforts in Human Computer Interaction (HCI) are focused to allow personal computers (PCs) to analyze human feelings. Although, some researchers are trying to realize human-machine interfaces with an emotion understanding capability. In this study, an effective emotion recognition framework is proposed dependent on the support vector machine (SVM) classifier. The proposed classifier employs a versatile technique for combined element extraction, which uses the methods of empirical mode decomposition (EMD) and kernel density estimation (KDE). The technique used in the proposed classifier decomposes the signal and accomplishes feature extraction with diminished computational unpredictability. The re-enactment results show that the proposed acknowledgement framework can accomplish 92.991% precision, and the correlation with some ordinary acknowledgement framework is additionally given.
KeywordsEmotion recognition EEG signal Independent component analysis (ICA) Fused feature extraction Empirical mode decomposition (EMD) Kernel density estimation (KDE)
I thank my co-author Dharmapal Dronacharya Doye for guiding me to complete this research and also am very thankful to my institution Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded, Maharashtra, India, for giving me full support to complete this work.
Conflict of Interest
Sujata Bhimrao Wankhade and Dharmapal Dronacharya Doye state that there are no conflicts of interest. Patients’ rights and animal protection statements: This research article does not contain any studies with human or animal subjects.
Animal and human subjects were not used in this study.
This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
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