Automated accurate emotion classification using Clefia pattern-based features with EEG signals

2022-11-01
Doğan, Abdullah
Barua, Prabal Datta
BAYĞIN, MEHMET
Tuncer, Turker
DOĞAN, ŞENGÜL
YAMAN, ORHAN
Doğru, Ali Hikmet
Acharya, Rajendra U.
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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.
Clefia pattern, emotion classification, TQWT, mRMR, majority voting, FEATURE-EXTRACTION, RECOGNITION, DATABASE
https://hdl.handle.net/11511/101552
INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT
Department of Computer Engineering, Article

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Citation Formats
A. Doğan et al., “Automated accurate emotion classification using Clefia pattern-based features with EEG signals,” INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT, pp. 0–0, 2022, Accessed: 00, 2023. [Online]. Available: https://hdl.handle.net/11511/101552.
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