Human Emotion Detection and Stress Analysis using EEG Signal
Prashant Lahane1, Mythili Thirugnanam2

1Prashant Lahane, Research Scholar, School of Computer Science and Engineering, VIT University, Vellore, Tamil Nadu, India.

2Mythili Thirugnanam, Associate Professor, School of Computer Science and Engineering, VIT University, Tamil Nadu, India.

Manuscript received on 05 March 2019 | Revised Manuscript received on 12 March 2019 | Manuscript Published on 20 March 2019 | PP: 96-100 | Volume-8 Issue- 4S2 March 2019 | Retrieval Number: D1S0021028419/2019©BEIESP

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Abstract: Stress has become a universal emotion that people experience in day to day life. In this paper, emotion detection is carried out using benchmark DEAP dataset by implementing a new feature extraction techniques named as Teager-Kaiser Energy Operator (TKEO) with a k-nearest neighbor (KNN), neural network(NN) and Classification Tree (CT) classifiers based on Electroencephalography (EEG). The study evaluates the performance and accuracy of emotion detection which is further used for stress identification as EEG gives good correlation with stress. Also, the present work compares the implemented TKEO feature extraction technique with Relative Energy Ratio (RER), and Kernel Density Estimation (KDE) techniques regarding accuracy. This paper demonstrates how the inclusion of TKEO enhances feature extraction and proves a promising approach to emotion detection as compared to other conventional techniques. The experimental results show that TKEO when used with KNN, NN, CT classifier gives comparatively higher accuracy than KDE and RER for channel 1 alpha band and channel 17 beta band for stress detection.

Keywords: Electroencephalography (EEG), Feature Extraction, Teager-Kaiser Energy Operator (TKEO), Neural Network, k-nearest Neighbor (KNN), a Bandpass Filter (BPF).
Scope of the Article: Computer Science and Its Applications