Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Area of Science:

• Neuroscience
• Computer Science
• Artificial Intelligence

Background:

• Emotion recognition is crucial in medicine and human-computer interaction.
• Physiological signals, including electroencephalography (EEG), fluctuate with emotional changes.
• EEG reflects brain activity, offering a pathway for emotion detection.

Purpose of the Study:

• To review the application of machine learning algorithms for emotion recognition using EEG signals.
• To provide an overview of EEG signal processing for emotion detection.
• To guide beginners in the field of EEG-based emotion recognition.

Main Methods:

• Acquisition and preprocessing of EEG signals.
• Feature extraction from EEG data to identify emotional states.
• Classification of extracted features using machine learning algorithms.

Main Results:

• Machine learning effectively extracts emotion-related features from EEG signals.
• Classifiers can distinguish discrete emotional states from EEG data.
• The field shows broad development prospects, particularly between 2016-2021.

Conclusions:

• EEG combined with machine learning offers a viable method for emotion recognition.
• This review aids researchers in understanding the current status of EEG-based emotion recognition.
• The integration of EEG and machine learning holds significant potential for future advancements.