A systematic literature review of emotion recognition using EEG signals

In this study, we conducted a systematic literature review of 107 primary studies conducted between 2017 and 2023 to discern trends in datasets, classifiers, and contributions to human emotion recognition using EEG signals. We identified DEAP (43%), SEED (29%), DREAMER (8%), and SEED-IV (5%) as the...

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Bibliographic Details
Published in:Cognitive systems research Vol. 82; p. 101152
Main Authors: Prabowo, Dwi Wahyu, Nugroho, Hanung Adi, Setiawan, Noor Akhmad, Debayle, Johan
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2023
Elsevier
Subjects:
Artificial Intelligence
Chemical and Process Engineering
Classifier
Computer Science
Dataset
EEG signals
Emotion recognition
Engineering Sciences
Feature extraction
Human-Computer Interaction
Neural and Evolutionary Computing
Research contribution
Emotion recognition
Feature extraction
EEG signals
Classifier
Dataset
Research contribution
ISSN:1389-0417, 1389-0417
Online Access:Get full text
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Summary:In this study, we conducted a systematic literature review of 107 primary studies conducted between 2017 and 2023 to discern trends in datasets, classifiers, and contributions to human emotion recognition using EEG signals. We identified DEAP (43%), SEED (29%), DREAMER (8%), and SEED-IV (5%) as the most commonly used EEG signal datasets. Deep learning techniques, especially transformer neural architecture search (TNAS), global-to-local feature aggregation network (GLFANet), attention-based convolutional transformer neural network (ACTNN) and efficient CNN-contrastive learning (ECNN-C), demonstrate superior performance across different datasets. Our comparative analysis of the validation scenarios revealed that subject-dependent validations outperformed subject-independent validations, highlighting the challenge of individual differences. This finding underscores the need for future research to address these individual differences in emotion recognition and enhance the generalizability of the models. The research contributions spanned seven categories: data augmentation, domain adaptation, channel selection, preprocessing, feature selection, classifiers, and feature extraction, with a major emphasis on feature extraction and classification (34%). This trend suggests a growing recognition of the importance of these contributions in the development of effective emotion-recognition systems. Our study contributes to the field by providing a comprehensive overview of recent trends, illuminating the performance of various classifiers, and highlighting key areas for future research on EEG-based emotion recognition. This work has significant implications for various applications, including mental health, human–computer interaction, and affective brain–computer interfaces.
ISSN:1389-0417
1389-0417
DOI:10.1016/j.cogsys.2023.101152