A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications

Abstract

Emotion recognition utilizing EEG signals has emerged as a pivotal component of human-computer interaction. In recent years, with the relentless advancement of deep learning techniques, using deep learning for analyzing EEG signals has assumed a prominent role in emotion recognition. Applying deep learning in the context of EEG-based emotion recognition carries profound practical implications. Although many model approaches and some review articles have scrutinized this domain, they have yet to undergo a comprehensive and precise classification and summarization process. The existing classifications are somewhat coarse, with insufficient attention given to the potential applications within this domain. Therefore, this article systematically classifies recent developments in EEG-based emotion recognition, providing researchers with a lucid understanding of this field's various trajectories and methodologies. Additionally, it elucidates why distinct directions necessitate distinct modeling approaches. In conclusion, this article synthesizes and dissects the practical significance of EEG signals in emotion recognition, emphasizing its promising avenues for future application.

Keywords: Deep learning; Electroencephalogram (EEG); Emotion recognition; Human computer interaction.

Figure 1. The publication and citation rate of articles on emotion recognition utilizing EEG signals continues to rise rapidly.

(Data Source: Web of Science Citation Report; Keyword: EEG, emotion recognition, emotional classification, and emotional computation; Date Range: January 1st, 2004, to March 18th, 2024; Database Coverage: All databases except preprints).

Figure 2. Flowchart of the article screening process.

Figure 3. Flowchart of survey methodology.

Figure 4. Subject-dependent technology roadmap.

Figure 5. Subject-independent technology roadmap.