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ABSTRACT
Background: The electroencephalogram (EEG) emotion classification/recognition is one of the popular issues for advanced signal classification. However, it is difficult to manually screen the EEG signals as they are highly nonlinear and non-stationary.
Methods: This paper introduces a novel nonlinear and multileveled features-based automatic EEG emotion classification method. Our presented EEG classification model uses feature vector creation deploying an S-Box-based local pattern with a decomposition (tunable q-factor wavelet transform is utilized), the most significant features chosen, classification a shallow machine learning method, and hard majority voting. The novel side of this research is the presented feature extractor since a component of the Clefia cipher has been considered to create a local feature extractor.
Results: We have obtained an accuracy of 100.0%, 98.02%, 99.33%, and for valence, arousal, and dominance cases using the DEAP database. Also, we achieved 99.69%, 98.98%, and 99.66% accuracies for valence, dominance, and arousal cases with the DREAMER database. Our proposed model is able to classify arousal, dominance, and valence cases with an accuracy of more than 98% using both databases.
Conclusions: The results show that the clefia pattern can perform automatic emotion classification with low computational complexity and high accuracy.
Disclosure statement
No potential conflict of interest was reported by the author(s).
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Notes on contributors
Abdullah Dogan
Abdullah Dogan is currently continuing his doctorate education in the Department of Computer Engineering at Middle East Technical University, Ankara, Turkiye. His research interests include image processing, signal processing, and pattern recognition.
Prabal Datta Barua
Prabal Datta Barua is an Adjunct Professor at the University of Southern Queensland and an Honorary Industry Fellow at the University of Technology Sydney. He is interested in AI technologies and published several papers in the Q1 journal. He is an industry leader in ICT entrepreneurship in Australia.
Mehmet Baygin
Mehmet Baygin is currently an Associate Professor doctor in the Department of Computer Engineering, Ardahan University, Turkiye. His research interests include machine learning, computer vision, signal processing, blockchain and photovoltaic systems.
Turker Tuncer
Turker Tuncer is currently an Associate Professor with the Digital Forensics Engineering, Technology Faculty, Firat University, Turkiye. His main research interests include feature engineering, image processing, signal processing, information security, and pattern recognition.
Sengul Dogan
Sengul Dogan is currently an Associate Professor with the Digital Forensics Engineering, Technology Faculty, Firat University, Turkiye. Her main research interests include computer forensics, mobile forensics, image processing, and signal processing.
Orhan Yaman
Orhan Yaman is currently an Associate Professor with the Digital Forensics Engineering, Technology Faculty, Firat University, Turkiye. His main research interests include image processing, signal processing, the internet of things, machine learning, embedded systems, and fuzzy systems.
Ali Hikmet Dogru
Ali Hikmet Dogru is currently an Professor with the Computer Engineering, Middle East Technical University, Ankara, Turkiye. His main research interests include software engineering, software development methodologies software architecture, software product lines, artificial intelligence.
Rajendra U. Acharya
U. R. Acharya is ranked in the top 1% of the Highly Cited Researchers for the past six consecutive years (2016 – 2022) in Computer Science according to the Essential Science Indicators of Thomson. Senior Editor of Computers in Biology Medicine (CBM) and Associate Editor of 8 ISI Journals.