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Electroencephalogram-based emotion Recognition: A systematic review on deep learning methods

human-computer-interaction
artificial-intelligence
data-mining-and-machine-learning
optimization-theory-and-computation
neural-networks

Abstract

Emotion recognition serves as a cornerstone for achieving natural human-machine interaction and represents a crucial prerequisite in the advancement toward human-like general intelligence. This study provides a systematic review of deep learning-based electroencephalogram (EEG) emotion recognition research over the past decade, based on a bibliometric analysis of 908 publications and 20,837 citations. We employ bibliometric analysis to offer a panoramic view of the research landscape and identify hotspots and trends in this domain. The analysis reveals increasing scholarly momentum in the field, characterized by growing interdisciplinary collaboration. China and the United States maintain their positions as primary research hubs within the global research network. The field has witnessed significant methodological standardization, particularly in crucial aspects such as data acquisition protocols, model input processing, and architectural frameworks. Model innovation is highly active, with widespread adoption of deep learning approaches, notably convolutional neural networks and transfer learning techniques, while interest in Transformer and graph convolutional networks is rising. This study categorizes the real-world deployment of EEG-based emotion recognition across multiple domains, including emotional disorder detection, consumer preference analysis, and intelligent education systems. Furthermore, this study identifies and summarizes ten emerging trends, covering advancements in emotion classification models, data optimization, cutting-edge algorithms, individual differences processing, and practical applications .
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