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A Survey on Feature Extraction Methods for EEG Based Emotion Recognition

  • Souvik PhadikarEmail author
  • Nidul Sinha
  • Rajdeep Ghosh
Conference paper
  • 74 Downloads
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 12)

Abstract

In recent times, emotion recognition is in attention in brain computer interface (BCI) and human computer interaction (HCI) research area to provide a very good communication between brain and computer. The aim is to achieve a good recognition rate, although there are numerous researches have been conducted also there has been created several confusions with the definition of human emotions and the difference between emotions and moods. To detect brain signal, Electroencephalogram (EEG) signal has become biological marker. For its low cost, good time and spatial resolution EEG has been used widely in BCI researches. Extraction of features from EEG signals is one of the vital steps of EEG based emotion recognition. The appropriate feature selection for EEG based automatic emotion recognition is still now a big research topic. In this paper, a comprehensive survey is made on feature extraction methods and their comparative merits and limitations.

Keywords

Emotions Electroencephalography (EEG) Feature extraction methods Comparison 

Notes

Acknowledgment

We would like to thank TEQIP III, NIT Silchar for all the support for carrying out the presented review paper work.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical EngineeringNIT SilcharSilcharIndia
  2. 2.Department of Information TechnologyGauhati UniversityGauhatiIndia

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