A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals

Emotion recognition using electroencephalography (EEG) is becoming an interesting topic among researchers. It has made a remarkable entry in the domain of biomedical, smart environment, brain-computer interface (BCI), communication, security, and safe driving. In the past decade, several studies hav...

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Bibliographic Details
Published inMultimedia tools and applications Vol. 82; no. 18; pp. 27269 - 27304
Main Authors Kamble, Kranti, Sengupta, Joydeep
Format Journal Article
LanguageEnglish
Published New York Springer US 01.07.2023
Springer Nature B.V
Subjects
Algorithms
Artificial intelligence
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Deep learning
Electroencephalography
Emotion recognition
Emotions
Human-computer interface
Machine learning
Multimedia Information Systems
Special Purpose and Application-Based Systems
Emotion recognition
EEG
SEED-IV
BCI
DEAP
Deep learning
DREAMER
Interfaces
SEED-V
MAHNOB
Machine learning
SEED
Musec
Artificial intelligence
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Summary:Emotion recognition using electroencephalography (EEG) is becoming an interesting topic among researchers. It has made a remarkable entry in the domain of biomedical, smart environment, brain-computer interface (BCI), communication, security, and safe driving. In the past decade, several studies have been published that viewed emotion recognition tasks in a variety of manners. Multiple algorithms have been developed to accurately capture the EEG signal and identify the emotions from such EEG signals. The advent of artificial intelligence (AI) has changed the landscape of every application including emotion recognition. Two categories of AI-based algorithms such as machine learning and deep learning algorithms are becoming popular in the emotion recognition domain. This narrative review is an attempt to provide deep insight into the AI-based techniques, their role in EEG-based emotion recognition, and their potential future possibilities in accurate emotion identification. Furthermore, this review also provides an overview of the several important topics in emotion recognition such as emotion paradigms, EEG and its processing, and the public databases.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-023-14489-9