Detection of human emotions using features based on discrete wavelet transforms of EEG signals

  • Authors

    • The jaswini S
    • K M Ravikumar
    https://doi.org/10.14419/ijet.v7i1.9.9746

    Received date: February 26, 2018

    Accepted date: February 26, 2018

    Published date: March 1, 2018

  • Affective Computing, EEG Signals, DWT, ANN, SVM, KNN.

  • Abstract

    Affective computing is an emerging area of research in human computer interaction where researchers have developed automated assessment of human emotion states using physiological signals to establish affective human compute interactions. In this paper wepresent an efficient algorithm for emotion recognition using EEG signals for the data acquired by audio- video stimuli. The desired frequency bands are extracted using discrete wavelet transforms. The Statistical features, Hjorth parameters, differential entropy and wavelet features are extracted. Artificial neural networks, Support Vector Machine (SVM) and K- nearest neighbor are used on the extracted feature set to develop prediction models and to classify into four emotion states like clam, happy, fear and sad . These Artificial neural network models are evaluated on the acquired dataset and emotions are classified into four different states with over all accuracy of 86.36%. The classification rate of calm, happy, fear and sad states are 90.9%, 63.63%, 90.90 and 100 % respectively.

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  • How to Cite

    jaswini S, T., & M Ravikumar, K. (2018). Detection of human emotions using features based on discrete wavelet transforms of EEG signals. International Journal of Engineering and Technology, 7(1.9), 119-122. https://doi.org/10.14419/ijet.v7i1.9.9746