2020
DOI: 10.3389/fnins.2020.622759
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An Investigation of Deep Learning Models for EEG-Based Emotion Recognition

Abstract: Emotion is the human brain reacting to objective things. In real life, human emotions are complex and changeable, so research into emotion recognition is of great significance in real life applications. Recently, many deep learning and machine learning methods have been widely applied in emotion recognition based on EEG signals. However, the traditional machine learning method has a major disadvantage in that the feature extraction process is usually cumbersome, which relies heavily on human experts. Then, end… Show more

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Cited by 91 publications
(43 citation statements)
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“…Deep neural networks have received a lot of attention over the last decade and have been the primary tool of choice for automation in several application areas, including biomedical engineering. In EEG applications deep neural networks have been employed for emotion recognition (Zhang et al, 2020 ) and motor imagery classification (Wu et al, 2019 ). However, deep neural networks are known to be data hungry and require a significant amount of labeled data for training.…”
Section: Introductionmentioning
confidence: 99%
“…Deep neural networks have received a lot of attention over the last decade and have been the primary tool of choice for automation in several application areas, including biomedical engineering. In EEG applications deep neural networks have been employed for emotion recognition (Zhang et al, 2020 ) and motor imagery classification (Wu et al, 2019 ). However, deep neural networks are known to be data hungry and require a significant amount of labeled data for training.…”
Section: Introductionmentioning
confidence: 99%
“…Deep learning is a promising subfield of machine learning, which has successful applications in many fields, such as natural language processing and computer vision, etc ( Huang et al, 2021 ). Recently, it has attracted more attention in BCIs ( Dose et al, 2018 ; Ming et al, 2018 ; Liu Y. et al, 2020 ; Zhang Y. et al, 2020 ). It can build a unified end-to-end model directly applied to raw EEG signals.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, a growing body of research investigated emotionrecognition with deep learning algorithms. Particularly convolutional neural networks showed promising results, outperforming traditional ML models in distinguishing emotional states (Aloysius and Geetha, 2017;Li et al, 2018;Moon et al, 2018;Yang et al, 2019;Zhang et al, 2020). However, these models are still in their infancy and have various limitations for EEG-based BCI classification due to the limited training data available (Lotte et al, 2018).…”
Section: Introductionmentioning
confidence: 99%