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Review on Emotion Recognition Based on Electroencephalography.

Haoran Liu1, Ying Zhang2, Yujun Li1

  • 1The Boiler and Pressure Vessel Safety Inspection Institute of Henan Province, Zhengzhou, China.

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|October 18, 2021
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Summary

Electroencephalography (EEG) signals reveal distinct emotional states, aiding in emotion recognition for various applications. This review details EEG-based emotion recognition methods and their effectiveness.

Keywords:
DEAPDREAMEREEGSEEDconvolution neural networkemotion recognition

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Area of Science:

  • Neuroscience
  • Computer Science
  • Psychology

Background:

  • Emotions significantly influence human behavior, family dynamics, and societal interactions.
  • Electroencephalography (EEG) signals exhibit variations corresponding to different emotional states, offering a non-disguisable indicator.
  • EEG-based emotion recognition is increasingly vital in human-computer interaction, medical diagnostics, and military applications.

Purpose of the Study:

  • To outline the fundamental steps involved in developing an EEG-based emotion recognition algorithm.
  • To review current methodologies in EEG-based emotion recognition.
  • To evaluate the classification performance of existing EEG-based emotion recognition techniques.

Main Methods:

  • Data acquisition from electroencephalography (EEG) recordings.
  • Preprocessing of raw EEG signals to remove artifacts.
  • Feature extraction and selection to identify discriminative patterns.
  • Classification of emotional states using machine learning models.

Main Results:

  • The paper details a systematic approach to EEG-based emotion recognition.
  • A comprehensive review of existing EEG-based emotion recognition methods is presented.
  • The classification efficacy of various methods is assessed.

Conclusions:

  • This work provides a foundational understanding of EEG-based emotion recognition for researchers.
  • It serves as a reference for advancing future research and development in the field.
  • Emotion recognition using EEG is crucial for understanding safety psychology.