PS-VTS: particle swarm with visit table strategy for automated emotion recognition with EEG signals

Yagmur Olmez; Gonca Ozmen Koca; Abdulkadir Sengur; U Rajendra Acharya

doi:10.1007/s13755-023-00224-z

PS-VTS: particle swarm with visit table strategy for automated emotion recognition with EEG signals

Health Inf Sci Syst. 2023 May 4;11(1):22. doi: 10.1007/s13755-023-00224-z. eCollection 2023 Dec.

Authors

Yagmur Olmez¹, Gonca Ozmen Koca¹, Abdulkadir Sengur², U Rajendra Acharya³

Affiliations

¹ Department of Mechatronics Engineering, University of Firat, 23119 Elazig, Turkey.
² Department of Electrical and Electronics Engineering, University of Firat, 23119 Elazig, Turkey.
³ School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia.

PMID: 37151916
PMCID: PMC10160266 (available on 2024-12-01)
DOI: 10.1007/s13755-023-00224-z

Abstract

Recognizing emotions accurately in real life is crucial in human-computer interaction (HCI) systems. Electroencephalogram (EEG) signals have been extensively employed to identify emotions. The researchers have used several EEG-based emotion identification datasets to validate their proposed models. In this paper, we have employed a novel metaheuristic optimization approach for accurate emotion classification by applying it to select both channel and rhythm of EEG data. In this work, we have proposed the particle swarm with visit table strategy (PS-VTS) metaheuristic technique to improve the effectiveness of EEG-based human emotion identification. First, the EEG signals are denoised using a low pass filter, and then rhythm extraction is done using discrete wavelet transform (DWT). The continuous wavelet transform (CWT) approach transforms each rhythm signal into a rhythm image. The pre-trained MobilNetv2 model has been pre-trained for deep feature extraction, and a support vector machine (SVM) is used to classify the emotions. Two models are developed for optimal channels and rhythm sets. In Model 1, optimal channels are selected separately for each rhythm, and global optima are determined in the optimization process according to the best channel sets of the rhythms. The best rhythms are first determined for each channel, and then the optimal channel-rhythm set is selected in Model 2. Our proposed model obtained an accuracy of 99.2871% and 97.8571% for the classification of HA (high arousal)-LA (low arousal) and HV (high valence)-LV (low valence), respectively with the DEAP dataset. Our generated model obtained the highest classification accuracy compared to the previously reported methods.

Keywords: Channel; Deep features; EEG signals; Metaheuristic optimization; Rhythm selections.

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