An Approach to EEG Based Emotion Recognition and Classification using Kernel Density Estimation

被引：20
|
作者：
Lahane, Prashant [1 ]
Sangaiah, Arun Kumar [2 ]
机构：
[1] MIT Coll Engn, Dept Comp Engn, Pune, Maharashtra, India
[2] VIT Univ, Sch Comp Sci & Engn, Vellore, Tamil Nadu, India
来源：
INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION AND CONVERGENCE (ICCC 2015) | 2015年 / 48卷
关键词：
Electroencephalography (EEG) Signals; Independent Component Analysis (ICA); Kernel Density Estimation (KDE); Artificial Neural Network (ANN);
D O I：
10.1016/j.procs.2015.04.138
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper aims to proposed emotion recognition using electroencephalography (EEG) techniques. Recognizing emotion by using computers is becoming popular these days. This paper is based on calculating EEG signals and recognizing emotion from human brain activity. Electroencephalogram (EEG) signals are taken from the scalp of the brain and assessed in responds to several stimuli from the four basic emotions on the IAPS emotion stimuli. Features from the EEG signals are captured using the Kernel Density Estimation (KDE) and classified via the artificial neural network classifier to recognise emotional condition of the subject under test. Results are obtained to prove that the proposed modified KDE gives better results in terms of accuracy. Also, the proposed method gives better estimation of emotion of the subject from streaming EEG data by using the concept of cluster kernels. (C) 2015 The Authors. Published by Elsevier B.V.
引用
收藏
页码：574 / 581
页数：8
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