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Brain-Computer Interfaces pp 215–240Cite as

Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 74))

Abstract

Emotion classification based on electroencephalogram (EEG) signals is a relatively new area of research in the development of brain computer interface (BCI) system with challenging issues like induction of the emotional states and the extraction of the features in order to obtain optimum classification of human emotions. The emotion classification system based on BCI can be useful in many areas like as entertainment, education, and health care. This chapter presents a new method for human emotion classification using multiwavelet transform of EEG signals. The EEG signal contains useful information related to the different emotional states, which helps us to understand the psychology and neurology of the human brain. The features namely, ratio of the norms based measure, Shannon entropy measure, and normalized Renyi entropy measure are computed from the sub-signals generated by multiwavelet decomposition of EEG signals. These features have been used as an input to multiclass least squares support vector machine (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for classification of human emotions from EEG signals. The classification performance of the proposed method for classification of emotions using EEG signals determined by computing the classification accuracy, ten-fold cross-validation, and confusion matrix. The proposed method has provided classification accuracy of 84.79 % for classification of human emotions namely happy, neutral, sadness, and fear from EEG signals with Morlet wavelet kernel function of MC-LS-SVM. The audio–video stimulus has been used for inducing the emotions in EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of human emotions from EEG signals.

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Acknowledgements

Financial support obtained from the Department of Science and Technology (DST) India, Fast track project titled “Analysis and Classification of EEG Signals based on Nonlinear and Non-stationary Signal Models”, project number SR/FTP/ETA-90/2010 is greatly acknowledged.

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Authors and Affiliations

  1. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, 452017, India

    Varun Bajaj & Ram Bilas Pachori

  2. Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, 482005, India

    Varun Bajaj

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  1. Varun Bajaj
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Correspondence to Varun Bajaj .

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    Aboul Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

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Bajaj, V., Pachori, R.B. (2015). Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals. In: Hassanien, A., Azar, A. (eds) Brain-Computer Interfaces. Intelligent Systems Reference Library, vol 74. Springer, Cham. https://doi.org/10.1007/978-3-319-10978-7_8

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