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Classifying distinct emotions from parents of ASD child using EEG source data by combining Bernoulli–Laplace Prior and graph neural networks

  • S.I. : Fuzzy Logic and Probabilistic Modelling of Uncertain Information Systems.
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Abstract

Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-making, has made automatic emotion recognition and examination of a significant feature in the field of psychiatric disease treatment and cure. The problem arises from the limited spatial resolution of EEG recorders. Predetermined quantities of electroencephalography (EEG) channels are used by existing algorithms, which combine several methods to extract significant data. The major intention of this study was to focus on enhancing the efficiency of recognizing emotions using signals from the brain through an experimental, adaptive selective channel selection approach that recognizes that brain function shows distinctive behaviors that vary from one individual to another individual and from one state of emotions to another. We apply a Bernoulli–Laplace-based Bayesian model to map each emotion from the scalp senses to brain sources to resolve this issue of emotion mapping. The standard low-resolution electromagnetic tomography (sLORETA) technique is employed to instantiate the source signals. We employed a progressive graph convolutional neural network (PG-CNN) to identify the sources of the suggested localization model and the emotional EEG as the main graph nodes. In this study, the proposed framework uses a PG-CNN adjacency matrix to express the connectivity between the EEG source signals and the matrix. Research on an EEG dataset of parents of an ASD (autism spectrum disorder) child has been utilized to investigate the ways of parenting of the child's mother and father. We engage with identifying the personality of parental behaviors when regulating the child and supervising his or her daily activities. These recorded datasets incorporated by the proposed method identify five emotions from brain source modeling, which significantly improves the accuracy of emotion recognition in comparison with the existing algorithms. The results show a 1% to 2% increase in classification accuracy in absolute terms. Furthermore, an experiment indicates the proposed method performs better than similar methods. We also discovered that the suggested approach performs admirably when using conventional classification techniques.

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No datasets were generated or analyzed during the current study.

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  1. School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

    Stephen Dass ArulDass & Prabhu Jayagopal

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  1. Stephen Dass ArulDass
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  2. Prabhu Jayagopal
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All authors contributed to the design and methodology of this study, the assessment of the outcomes, and the writing of the manuscript.

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Correspondence to Prabhu Jayagopal.

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ArulDass, S.D., Jayagopal, P. Classifying distinct emotions from parents of ASD child using EEG source data by combining Bernoulli–Laplace Prior and graph neural networks. Neural Comput & Applic (2023). https://doi.org/10.1007/s00521-023-09171-y

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