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Emotion recognition in EEG signals using deep learning methods: A review.

Mahboobeh Jafari1, Afshin Shoeibi1, Marjane Khodatars1

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Deep learning (DL) shows promise for improving emotion recognition from electroencephalogram (EEG) signals, overcoming challenges like signal variability and individual differences for more objective emotion detection.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Signal Processing

Background:

  • Emotions significantly influence human cognition and interaction.
  • Physiological signals, particularly electroencephalogram (EEG), offer objective measures for emotion detection.
  • EEG's direct link to the central nervous system and high spatial resolution make it valuable for emotion recognition studies.

Purpose of the Study:

  • To examine the application of deep learning (DL) techniques for emotion recognition using EEG signals.
  • To discuss the challenges associated with EEG-based emotion recognition.
  • To highlight the potential of DL in addressing these challenges and suggest future research directions.

Main Methods:

  • Review and analysis of existing literature on DL techniques applied to EEG emotion recognition.
  • Exploration of challenges in EEG signal processing and feature extraction for emotion detection.
  • Discussion of artificial intelligence (AI) methods, including machine learning (ML) and DL, for handling complex EEG data.

Main Results:

  • EEG-based emotion recognition faces challenges such as signal variability, individual differences, and lack of standardized processing.
  • DL techniques demonstrate potential in overcoming these challenges due to their ability to handle complex and diverse EEG data.
  • Further research is needed to identify optimal features and develop more robust AI models for EEG emotion recognition.

Conclusions:

  • DL offers a promising avenue for advancing objective emotion recognition from EEG signals.
  • Addressing signal variability and individual differences is crucial for reliable EEG-based emotion detection.
  • Future research should focus on developing advanced DL models and standardized processing techniques for enhanced emotion recognition.