Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2540 2019 2019 Department of Information Technology, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Krishna district, Andhra Pradesh, India M. M. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, India M. S. Minu Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, India P. Vidyasri Department of Information Systems, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia Habeeb Omotunde Department of Computer Science and Engineering, R.M.D. Engineering College, Kavarapettai, Tamilnadu, India A. Tamizharasi Department of Electronics and Communication Engineering, Paavai Engineering College (Autonomous), Pachal, Namakkal, Tamil Nadu, India R. Logarasu School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India Rama Prabha K.. P. Department of ECE, Saveetha Engineering College, Chennai, Tamilnadu, India V. Subashree Recently, Emotion detection utilizing EEG signals develops popularity in domain of Human-Computer Interaction (HCI). EEG (electroencephalography) is a non-invasive approach, which processes electrical action from the brain through electrodes located in the scalp. An emotion recognition approach could not only be significant for healthy people among them disabled persons for detecting emotional changes and is utilized for different applications. It is significant to realize that emotion recognition in EEG indications is a difficult task owing to difficult and subjective nature of emotions. In recent times, Machine learning (ML) algorithms like Random Forests or Support Vector Machines (SVM) and Deep Learning (DL) systems namely Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN) are trained on EEG feature extracted and connected emotional labels for classifying the user emotional state. This study presents an Automated EEG-based Emotion Detection using Bonobo Optimizer with Deep Learning (AEEGED-BODL) technique on HCI applications. The goal of the study is to analyze the EEG signals for the classification of several kinds of emotions in HCI applications. To achieve this, the AEEGED-BODL technique uses Higuchi fractal dimension (HFD) approach for extracting features in the EEG signals. Besides, the AEEGED-BODL technique makes use of the quasi-recurrent neural network (QRNN) approach for the detection and classification of distinct kinds of emotions. Furthermore, the BO system was demoralized for optimum hyperparameter selection of QRNN model, which helps in attaining an improved detection rate. The simulation validation of AEEGED-BODL algorithm was simulated on EEG signal database. The comprehensive result stated best outcome of the AEEGED-BODL algorithm over other recent approaches 2024 2024 70 83 10.54216/JISIoT.120106 https://www.americaspg.com/articleinfo/18/show/2540