Automated accurate emotion classification using Clefia pattern-based features with EEG signals
INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT, cilt.17, sa.1, ss.32-45, 2024 (ESCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 17 Sayı: 1
- Basım Tarihi: 2024
- Doi Numarası: 10.1080/20479700.2022.2141694
- Dergi Adı: INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Business Source Elite, Business Source Premier
- Sayfa Sayıları: ss.32-45
- Anahtar Kelimeler: Clefia pattern, emotion classification, TQWT, mRMR, majority voting, FEATURE-EXTRACTION, RECOGNITION, DATABASE
- Orta Doğu Teknik Üniversitesi Adresli: Evet
Özet
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.