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.
Highlights
Emotions are the changes in people’s psychological and physiological states when they face external stimuli such as sounds, images, smells, temperature, and so on
This paper summarizes the development of EEG-based machine learning methods for emotion recognition from four aspects: acquisition, preprocessing, feature extraction, and feature classification
The classifier is a general term for the methods of classifying samples, and for emotion recognition using EEG signals, it is a crucial part, which takes the features extracted in the above process as input to complete the recognition of the emotional states
Summary
Emotions are the changes in people’s psychological and physiological states when they face external stimuli such as sounds, images, smells, temperature, and so on. What’s more, to effectively collect EEG signals, the attachment position of electrodes for EEG equipment in many studies follows the international 10–20 system (Chai et al, 2016; Seo et al, 2019; Hou et al, 2020; Huang, 2021) Another way is to use the existing, well-known database in the field of emotion recognition based on EEG, including DEAP (Izquierdo-Reyes et al, 2018), MAHNOB-HCI (Izquierdo-Reyes et al, 2018), GAMEEMO (Özerdem and Polat, 2017), SEED (Lu et al, 2020), LUMED (Cimtay and Ekmekcioglu, 2020), AMIGOS (Galvão et al, 2021), and DREAMER (Galvão et al, 2021). One way is to stimulate the subject to produce emotional changes by playing audio, video, or other materials and obtain the EEG signal through the EEG device worn by the subject. Yuvaraj et al (2014) obtained EEG data using
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