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

Abstract

Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

“…The proposed method considers only the Fp1-Fp2 channel pair from which the alpha band’s variance and PSD were computed, by that minimizing the computational overhead whilst achieving reliable performance making it suitable for wearable EEG headsets used in real-time applications [ 26 , 111 ]. Overall, the results attained here are quite promising.…”

“…In addition, individuals can use EEG to improve their productivity and wellness via monitoring their moods and emotions [ 25 ]. However, extracting meaningful information using few EEG channels in order to reduce the computational complexity of wearable headsets is still an ongoing challenge [ 26 , 27 ].…”

Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition

“…Feature extraction plays an important role in EEG emotion recognition systems. From the perspective of traditional signal processing, the temporal features, frequency features, and time–frequency features of the EEG signals are usually extracted [ 5 , 6 ]. Since it is difficult to mine the deep emotional features contained in EEG signals, it is a challenge to further improve the performance of the emotion recognition task.…”