Review on Support Vector Machine (SVM) classifier for human emotion pattern recognition from EEG signals

Noor Aishah Atiqah Zulkifli, Sawal Hamid Md Ali, Siti Anom Ahmad, Md. Shabiul Islam

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This study reviewed the strategy in pattern classification for human emotion recognition system based on Support Vector Machine (SVM) classifier on Electroencephalography (EEG) signal. SVM has been widely used as a classifier and has been reported as having minimum error and produce accurate classification. However, the accuracy is influenced by many factors such as the electrode placement, equipment used, preprocessing techniques and selection of feature extraction methods. There are many types of SVM classifier such as SVM via Radial Basis Function (RBF), Linear Support Vector Machine (LSVM) and Multiclass Least Squares Support Vector Machine (MC-LS-SVM). SVM via RBF states the average accuracy rate of 92.73, 85.41, 93.80 and 67.40% using different features extraction method, respectively. The accuracy using LSVM and MC-LS-SVM classifier are 91.04 and 77.15%, respectively. Although, the accuracy rate influenced by many factors in the experimental works, SVM always shows their function as a great classifier. This study will discuss and summarize a few related works of EEG signals in classifying human emotion using SVM classifier.

Original languageEnglish
Pages (from-to)135-146
Number of pages12
JournalAsian Journal of Information Technology
Volume14
Issue number4
DOIs
Publication statusPublished - 2015

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pattern recognition
extraction method
support vector machine
electrode

Keywords

  • Accuracy rate
  • Artifacts removal
  • Classifier method
  • Electroencephalography (EEG)
  • Emotion recognition
  • Support Vector Machine (SVM)

ASJC Scopus subject areas

  • Atmospheric Science

Cite this

Review on Support Vector Machine (SVM) classifier for human emotion pattern recognition from EEG signals. / Atiqah Zulkifli, Noor Aishah; Md Ali, Sawal Hamid; Ahmad, Siti Anom; Islam, Md. Shabiul.

In: Asian Journal of Information Technology, Vol. 14, No. 4, 2015, p. 135-146.

Research output: Contribution to journalArticle

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