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

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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.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Mechatronics Engineering, University of Firat, 23119, Elazig, Turkey

    Yagmur Olmez & Gonca Ozmen Koca

  2. Department of Electrical and Electronics Engineering, University of Firat, 23119, Elazig, Turkey

    Abdulkadir Sengur

  3. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia

    U. Rajendra Acharya

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  1. Yagmur Olmez
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  2. Gonca Ozmen Koca
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  3. Abdulkadir Sengur
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  4. U. Rajendra Acharya
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Correspondence to Abdulkadir Sengur.

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Appendix

Appendix

See Tables 9 and 10.

Table 9 Benchmark functions
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Table 10 Comparison of optimization results for 30 benchmark functions
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Olmez, Y., Koca, G.O., Sengur, A. et al. PS-VTS: particle swarm with visit table strategy for automated emotion recognition with EEG signals. Health Inf Sci Syst 11, 22 (2023). https://doi.org/10.1007/s13755-023-00224-z

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