EEG-Based BCI Emotion Recognition: A Survey

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.