A Novel Approach for Effective Emotion Recognition Using Double Truncated Gaussian Mixture Model and EEG

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N Murali Krishna 1,* J. Sirisha Devi 2 Srinivas Yarramalle 3

1. Department of Computer Science and Engineering, Vignan Institute of Technology and Science, JNTU (H) India

2. Department of Computer Science and Engineering, Institute of Aeronautical Engineering, JNTU (H) India

3. Department of IT, GITAM University, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2017.06.04

Received: 23 Aug. 2016 / Revised: 20 Dec. 2016 / Accepted: 6 Feb. 2017 / Published: 8 Jun. 2017

Index Terms

Emotion recognition, Doubly Truncated Gaussian Mixture Model, Encephalography


Most of the models projected in the literature on Emotion Recognition aims at recognizing the emotions from the mobilized persons in noise free environment and is subjected to the emotion recognition of an individual using a single word for testing and training. Literature available to identify the emotions in case of immobilized persons is confined to the results available from the machines only. In this process brain-computer interaction is utilized using neuro-scan machines like Encephalography (EEG), to identify the emotions of immobilized individuals. It uses the physiological signals available from EEG data extracted from the brain signals of immobilized persons and tries to determine the emotions, but these results vary from machine to machine, and there exists no standardization process which can identify the feelings of the brain diseased persons accurately. In this paper a novel method is proposed, Doubly Truncated Gaussian Mixture Model (DT-GMM) to have a complete emotion recognition system which can identify emotions exactly in a noisy environment from both the healthy individuals and sick persons. The results of the proposed system surpassed the accuracy rates of traditional systems.

Cite This Paper

N Murali Krishna, J Sirisha Devi, Srinivas Yarramalle,"A Novel Approach for Effective Emotion Recognition Using Double Truncated Gaussian Mixture Model and EEG", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.6, pp.33-42, 2017. DOI:10.5815/ijisa.2017.06.04


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