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EEG-Based BCI Emotion Recognition: A Survey.

Edgar P Torres P1, Edgar A Torres2, Myriam Hernández-Álvarez1

  • 1Escuela Politécnica Nacional, Facultad de Ingeniería de Sistemas, Departamento de Informática y Ciencias de la Computación, Quito, Ecuador.

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Summary
This summary is machine-generated.

This study surveys emotion recognition using electroencephalography (EEG)-based Brain Computer Interfaces (BCI). It analyzes computer science algorithms and trends from 2015-2020, offering insights for future affective computing research.

Keywords:
BCIclassificationemotionextractionfeaturepreprocessingrecognitionselectionsurveytrends

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Area of Science:

  • Affective computing and artificial intelligence.
  • Interdisciplinary research in human-computer interaction and neuroscience.

Background:

  • Human emotion recognition is increasingly explored using electroencephalography (EEG)-based Brain Computer Interfaces (BCI).
  • This field has diverse applications and is experiencing rapid growth.
  • A comprehensive literature review is needed to understand current trends and methodologies.

Purpose of the Study:

  • To conduct a scientific literature survey on emotion recognition using EEG-BCI from 2015 to 2020.
  • To present trends and comparative analyses of algorithms from a computer science perspective.
  • To identify future research directions in affective computing.

Main Methods:

  • Systematic review of scientific literature published between 2015 and 2020.
  • Analysis of datasets, emotion elicitation techniques, feature extraction/selection, and classification algorithms.
  • Comparative evaluation of algorithm performance in emotion recognition.

Main Results:

  • Identification of key trends in EEG-BCI based emotion recognition research.
  • Comparative analysis of various algorithms applied in recent implementations.
  • Overview of common datasets and methodologies used in the field.

Conclusions:

  • The field of emotion recognition using EEG-BCI is dynamic with evolving algorithmic approaches.
  • Understanding current trends in datasets, methods, and algorithms is crucial for future advancements.
  • Further research is needed to enhance accuracy and applicability of affective computing systems.