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

• Published in: Health information science and systems Vol. 11; no. 1; p. 22
• Main Authors: Olmez, Yagmur, Koca, Gonca Ozmen, Sengur, Abdulkadir, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 04-05-2023; BioMed Central Ltd; Springer Nature B.V
• Subjects: Arousal; Bioinformatics; Channels; Classification; Computational Biology/Bioinformatics; Computer Science; Continuous wavelet transform; Datasets; Discrete Wavelet Transform; Electroencephalography; Emotion recognition; Emotions; Feature extraction; Health Informatics; Heuristic methods; Human-computer interface; Information Systems and Communication Service; Low pass filters; Model accuracy; Optimization; Rhythm; Support vector machines; Wavelet transforms; Rhythm selections; Metaheuristic optimization; EEG signals; Deep features; Channel
• ISSN: 2047-2501; 2047-2501
• DOI: 10.1007/s13755-023-00224-z

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.