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Human Behaviour Analysis Using Intelligent Systems pp 179–204Cite as

Human Emotion Classification Using EEG Signals by Multivariate SynchroSqueezing Transform

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Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 6))

Abstract

Communication between human and computer is highly demandable in different applications. Possessing of cognitive skills of a human for human-computer interaction (HCI) is a non-trivial task. Classification of human emotion involving artificial intelligence is an emerging field. HCI finds applications in smart home, industry, personal health and for different purposes. One such HCI task is an automated human emotion recognition system. In this paper, new human emotion classification method based on Electroencephalogram (EEG) signal is proposed that leverages singular value decomposition (SVD) and Multivariate SynchroSqueezing Transform (MSST). Highly random, non-stationary EEG signal illustrates the electrical activity of the brain and contains useful information. To extract the hidden information/features, Multivariate SynchroSqueezing Transform is exploited. It is an adaptive, invertible transform to improve the quality of the time-frequency representation (TFR) by considering it along the frequency axis. Selected channels of EEG signal are decomposed by Multivariate SynchroSqueezing Transform to capture the frequency components and its amplitudes at any specific time slot. Then, singular value of the matrix is extracted by SVD as the feature vector. Then the most contributing features are selected using AdaBoost ensemble of decision tree classifiers which also leads to a reduction in feature dimensionality. And this reduced feature model acts as input to AdaBoost classifier in One against All (OAA) strategy to discriminate the emotional states in both two dimensional and three-dimensional model comprising Valence, Arousal and Dominance. Experimental results on DEAP dataset show that the proposed method yields classification accuracy of 97% for 2D emotional model and gives better performance than the state-of-art systems by nearly 7%.

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Author information

Authors and Affiliations

  1. Department of CSE, Kamaraj College of Engineering and Technology, Virudhunagar, India

    S. T. Veena

  2. Department of ECE, Dayananda Sagar Academy of Technology and Management, Bangalore, India

    M. N. Sumaiya

Authors
  1. S. T. Veena
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  2. M. N. Sumaiya
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Corresponding author

Correspondence to S. T. Veena .

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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Karunya University, Coimbatore, Tamil Nadu, India

    D. Jude Hemanth

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Veena, S.T., Sumaiya, M.N. (2020). Human Emotion Classification Using EEG Signals by Multivariate SynchroSqueezing Transform. In: Hemanth, D. (eds) Human Behaviour Analysis Using Intelligent Systems. Learning and Analytics in Intelligent Systems, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-35139-7_9

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