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A systematic literature review of emotion recognition using EEG signals by Dwi Wahyu Prabowo; Hanung Adi Nugroho; Noor Akhmad Setiawan; Johan Debayle is a Computer Science article available to read on EtoBox.

What is A systematic literature review of emotion recognition using EEG signals about?

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

Who reads A systematic literature review of emotion recognition using EEG signals?

Author

Dwi Wahyu Prabowo; Hanung Adi Nugroho; Noor Akhmad Setiawan; Johan Debayle

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)