Seyyed Abed Hosseini; Mohammad Bagher Naghibi-Sistani

Emotion recognition method using entropy analysis of EEG signals

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Author(s)

Seyyed Abed Hosseini ^1,* Mohammad Bagher Naghibi-Sistani ²

1. Control Field Islamic Azad University-Mashhad Branch, Mashhad, Iran

2. Electrical Engineering Department Ferdowsi University of Mashhad, Mashhad, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2011.05.05

Received: 22 Apr. 2011 / Revised: 26 May 2011 / Accepted: 23 Jun. 2011 / Published: 8 Aug. 2011

Index Terms

Approximate Entropy, EEG signals, Emotion recognition, Wavelet Entropy.

Abstract

This paper proposes an emotion recognition system using EEG signals, therefore a new approach to emotion state analysis by approximate (ApEn) and wavelet entropy (WE) is described. We have used EEG signals recorded during emotion in five channels (FP1, FP2, T3, T4 and Pz), under pictures induction environment (calm-neutral and negative excited) for participants. After a brief introduction to the concept, the ApEn and WE were extracted from two different EEG time series. The result showed that, the classification accuracy in two emotion states was 73.25% using the support vector machine (SVM) classifier. The simulations showed that the classification accuracy is good and the proposed methods are effective. During an emotion, the EEG is less complex compared to the normal, indicating reduction in active neuronal process in the brain.

Cite This Paper

Seyyed Abed Hosseini,Mohammad Bagher Naghibi-Sistani,"Emotion recognition method using entropy analysis of EEG signals", IJIGSP, vol.3, no.5, pp.30-36, 2011. DOI: 10.5815/ijigsp.2011.05.05

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International Journal of Image, Graphics and Signal Processing (IJIGSP)