A comparative analysis of emotion recognition from stimulated EEG signals

Garima Singh, Arindam Jati, Anwesha Khasnobish, Saugat Bhattacharyya, Amit Konar, D. N. Tibarewala, R. Janarthanan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a scheme to utilize the unaltered direct outcome of brain’s activity viz. EEG signals for emotion detection that is a prerequisite for the development of an emotionally intelligent system. The aim of this work is to classify the emotional states experimentally elicited in different subjects, by extracting their features for the alpha, beta, and theta frequency bands of the acquired EEG data using PSD, EMD, wavelet transforms, statistical parameters, and Hjorth parameters and then classifying the same using LSVM, LDA, and kNN as classifiers for the purpose of categorizing the elicited emotions into the emotional states of neutral, happy, sad, and disgust. The experimental results being a comparative analysis of the different classifier performances equip us with the best accurate means of emotion recognition from the EEG signals. For all the eight subjects, neutral emotional state is classified with an average classification accuracy of 81.65%, highest among the other three emotions. The negative emotions including sad and disgust have better average classification accuracy of 76.20 and 74.96% as opposed to the positive emotion i.e., happy emotional state, the average classification accuracy of which turns out to be 73.42%.

Original languageEnglish
Title of host publication2nd International Conference on Soft Computing for Problem Solving, SocProS 2012, Proceedings
EditorsB.V. Babu, Atulya Nagar, Jagdish Chand Bansal, Millie Pant, Kusum Deep, Kanad Ray, Umesh Gupta
PublisherSpringer Verlag
Pages1109-1115
Number of pages7
ISBN (Electronic)9788132216018
DOIs
Publication statusPublished - 1 Jan 2014
Event2nd International Conference on Soft Computing for Problem Solving, SocProS 2012 - Jaipur, India
Duration: 28 Dec 201230 Dec 2012

Publication series

NameAdvances in Intelligent Systems and Computing
Volume236
ISSN (Print)2194-5357

Conference

Conference2nd International Conference on Soft Computing for Problem Solving, SocProS 2012
CountryIndia
CityJaipur
Period28/12/1230/12/12

Keywords

  • Electroencephalogram (EEG)
  • Emotion recognition
  • Empirical mode decomposition (EMD)
  • Hjorth parameters
  • K-nearest neighbor (kNN)
  • Linear discriminant analysis (LDA)
  • Linear support vector machine (LSVM)
  • Power spectral density (PSD)
  • Statistical parameters (STAT)
  • Wavelet transform (WT)

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  • Cite this

    Singh, G., Jati, A., Khasnobish, A., Bhattacharyya, S., Konar, A., Tibarewala, D. N., & Janarthanan, R. (2014). A comparative analysis of emotion recognition from stimulated EEG signals. In B. V. Babu, A. Nagar, J. C. Bansal, M. Pant, K. Deep, K. Ray, & U. Gupta (Eds.), 2nd International Conference on Soft Computing for Problem Solving, SocProS 2012, Proceedings (pp. 1109-1115). (Advances in Intelligent Systems and Computing; Vol. 236). Springer Verlag. https://doi.org/10.1007/978-81-322-1602-5_116