Advancing EEG Research on Human Emotions Through Deep Learning Models

Wu, Yanbing · 2025 · Crossref

DOI: 10.56028/aetr.15.1.1594.2025

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Summary

This paper addresses the critical challenge of utilizing electroencephalography (EEG) for human emotion recognition, a field hindered by low signal-to-noise ratios, significant individual variability, and ethical privacy concerns. While EEG is established in medical diagnostics, its application in human-computer interaction requires robust methods to handle complex, noisy data. The study investigates how deep learning models can overcome these bottlenecks by automating feature extraction and classification, thereby enabling real-time, high-precision emotion detection for applications such as autonomous driving and smart homes. The authors propose an end-to-end processing framework centered on deep neural networks, specifically highlighting convolutional neural networks and generative adversarial networks (GANs). Unlike classical algorithms that rely on independent and identically distributed assumptions, these models automatically perform noise reduction, artifact removal, and frequency-band feature extraction. The paper outlines a "two-stage classification" strategy: first, coarse-grained emotion labels (e.g., happiness, anger) are extracted from time and frequency domains; second, emotions are refined into specific intensities (e.g., "mild pleasure" vs. "extreme excitement") using emotion-specific frequency bands. Dynamic emotional changes are captured using short-time Fourier transform and continuous wavelet transform. The study also reviews applications in other fields, such as automated agriculture and nicotine impact measurement, to demonstrate the efficacy of deep learning in processing complex waveform data. The findings indicate that deep learning models significantly reduce manual intervention in preprocessing and improve classification accuracy. The conclusion reports that combining adversarial generation with federated learning maintains a cross-individual accuracy rate of over 85%, even in scenarios with limited samples and strict privacy constraints. Furthermore, the introduction of a three-dimensional joint loss function allows the model to distinguish emotional categories effectively, supporting millisecond-level inference suitable for real-time applications. However, the study identifies three major shortcomings: existing datasets rely heavily on laboratory-induced emotions lacking natural context, network structures prioritize accuracy over interpretability, and ethical governance mechanisms like data withdrawal remain in early validation stages. The significance of this work lies in providing a technical pathway for EEG-based emotion recognition to move beyond laboratory settings. The authors conclude that future research must focus on building cross-cultural, long-term, multimodal open benchmarks to address generalization issues. Additionally, developing interpretable models with causal reasoning capabilities is essential to map deep features to verifiable neural mechanisms. Finally, the paper emphasizes the need for standardized collaboration across industries and the establishment of unified protocols to ensure that technological innovation aligns with user dignity and privacy protection.

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