Electro Ence Phalography and Physiological Signals for Emotion Analysis
J Sirisha Devi1, Siva Prasad Nandyala2

1Dr. J Sirisha Devi, Department of Computer Science and Engineering, Institute of Aeronautical Engineering, JNTU H, Hyderabad (Telangana), India.
2Dr. Siva Prasad Nandyala, DSP Specialist, Tata Elxsi Bangalore (Karnataka), India
Manuscript received on 05 February 2019 | Revised Manuscript received on 13 February 2019 | Manuscript published on 28 February 2019 | PP: 293-297 | Volume-8 Issue-4, February 2019 | Retrieval Number: D2753028419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: A novel method for Electroencephalography (EEG) based emotion analysis using Gray Level Co-occurrence Matrix1 (GLCM) features contrast, correlation, energy, and homogeneity has been discussed with peripheral physiological signals. Emotions are classified using Linear Discriminant Analysis (LDA) and obtained an accuracy of 93.8. The proposed novel method discussed the effect of distances, and direction on GLCM features for different emotions. This paper concluded that GLCM features are an effective measure to discriminate the emotions and give significant knowledge for each emotion. The proposed novel methodology can be used as a tool for emotion analysis and it can also be useful for observing brain lobe variation globally.
Keyword: Electro Ence Phalography, Gray Level Co Occurrence Matrix1, Physiological Signals, Linear Discriminant Analysis.
Scope of the Article: Measurement & Performance Analysis