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September 10, 2025Open Access

EEG-ERnet: Emotion Recognition based on Rhythmic EEG Convolutional Neural Network Model

Key Points

The EEG-ERnet model achieved a classification accuracy of 93.27% for valence detection, highlighting its effectiveness.
Using 10-fold cross-validation, the model demonstrated consistent performance across different emotional dimensions, enhancing BCI utility.
Power spectral densities were transformed into rhythmic images, optimizing the recognition of emotional states through advanced CNN techniques.
Subject-independent classification was emphasized, addressing variability in emotional responses and improving real-world applicability.

Abstract

Background: Emotion recognition from electroencephalography (EEG) can play a pivotal role in the advancement of brain-computer interfaces (BCIs). Recent developments in deep learning, particularly convolutional neural networks (CNNs) and hybrid models, have significantly enhanced interest in this field. However, standard convolutional layers often conflate characteristics across various brain rhythms, complicating the identification of distinctive features vital for emotion recognition. Furthermore, emotions are inherently dynamic, and neglecting their temporal variability can lead to redundant or noisy data, thus reducing recognition performance. Complicating matters further, individuals may exhibit varied emotional responses to identical stimuli due to differences in experience, culture, and background, emphasizing the necessity for subject-independent classification models. Methods: To address these challenges, we propose a novel network model based on depthwise parallel CNNs. Power spectral densities (PSDs) from various rhythms are extracted and projected as 2D images to comprehensively encode channel, rhythm, and temporal properties. These rhythmic image representations are then processed by a newly designed network, EEG-ERnet (Emotion Recognition Network), developed to process the rhythmic images for emotion recognition. Results: Experiments conducted on the dataset for emotion analysis using physiological signals (DEAP) using 10-fold cross-validation demonstrate that emotion-specific rhythms within 5-second time intervals can effectively support emotion classification. The model achieves average classification accuracies of 93.27 ± 3.05%, 92.16 ± 2.73%, 90.56 ± 4.44%, and 86.68 ± 5.66% for valence, arousal, dominance, and liking, respectively. Conclusions: These findings provide valuable insights into the rhythmic characteristics of emotional EEG signals. Furthermore, the EEG-ERnet model offers a promising pathway for the development of efficient, subject-independent, and portable emotion-aware systems for real-world applications.

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