PrimePatNet87: Prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition

Abdullah Dogan; Merve Akay; Prabal Datta Barua; Mehmet Baygin; Sengul Dogan; Turker Tuncer; Ali Hikmet Dogru; U Rajendra Acharya

doi:10.1016/j.compbiomed.2021.104867

PrimePatNet87: Prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition

Comput Biol Med. 2021 Nov:138:104867. doi: 10.1016/j.compbiomed.2021.104867. Epub 2021 Sep 16.

Authors

Abdullah Dogan¹, Merve Akay², Prabal Datta Barua³, Mehmet Baygin⁴, Sengul Dogan⁵, Turker Tuncer⁶, Ali Hikmet Dogru⁷, U Rajendra Acharya⁸

Affiliations

¹ Department of Computer Engineering, Middle East Technical University, Ankara, Turkey. Electronic address: adogan@ceng.metu.edu.tr.
² Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey. Electronic address: 182144105@firat.edu.tr.
³ School of Management & Enterprise, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, 2007, Australia. Electronic address: Prabal.Barua@usq.edu.au.
⁴ Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey. Electronic address: mehmetbaygin@ardahan.edu.tr.
⁵ Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey. Electronic address: sdogan@firat.edu.tr.
⁶ Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey. Electronic address: turkertuncer@firat.edu.tr.
⁷ Department of Computer Engineering, Middle East Technical University, Ankara, Turkey. Electronic address: dogru@metu.edu.tr.
⁸ Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan. Electronic address: aru@np.edu.sg.

PMID: 34543892
DOI: 10.1016/j.compbiomed.2021.104867

Abstract

Nowadays, many deep models have been presented to recognize emotions using electroencephalogram (EEG) signals. These deep models are computationally intensive, it takes a longer time to train the model. Also, it is difficult to achieve high classification performance using for emotion classification using machine learning techniques. To overcome these limitations, we present a hand-crafted conventional EEG emotion classification network. In this work, we have used novel prime pattern and tunable q-factor wavelet transform (TQWT) techniques to develop an automated model to classify human emotions. Our proposed cognitive model comprises feature extraction, feature selection, and classification steps. We have used TQWT on the EEG signals to obtain the sub-bands. The prime pattern and statistical feature generator are employed on the generated sub-bands and original signal to generate 798 features. 399 (half of them) out of 798 features are selected using minimum redundancy maximum relevance (mRMR) selector, and misclassification rates of each signal are evaluated using support vector machine (SVM) classifier. The proposed network generated 87 feature vectors hence, this model is named PrimePatNet87. In the last step of the feature generation, the best 20 feature vectors which are selected based on the calculated misclassification rates, are concatenated. The generated feature vector is subjected to the feature selection and the most significant 1000 features are selected using the mRMR selector. These selected features are then classified using an SVM classifier. In the last phase, iterative majority voting has been used to generate a general result. We have used three publicly available datasets, namely DEAP, DREAMER, and GAMEEMO, to develop our proposed model. Our presented PrimePatNet87 model reached over 99% classification accuracy on whole datasets with leave one subject out (LOSO) validation. Our results demonstrate that the developed prime pattern network is accurate and ready for real-world applications.

Keywords: EEG signal Classification; Emotion recognition; Hand-crafted method; Prime pattern network; mRMR selector.

MeSH terms

Algorithms*
Electroencephalography
Emotions
Humans
Support Vector Machine
Wavelet Analysis*