Abstract
Emotion recognition from EEG signals has emerged as a pivotal area of research, driven by its transformative potential in healthcare, brain-computer interfaces, and affective computing systems. However, the intrinsic complexity, non-linearity, and susceptibility to noise in EEG data present significant challenges to accurate emotional state classification. This study proposes a robust and interpretable hybrid deep learning model for EEG-based emotion recognition. The architecture integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and attention mechanisms, together with advanced signal processing techniques such as Continuous Wavelet Transform (CWT) and Power Spectral Density (PSD). This integrated approach facilitates the extraction of comprehensive spatial, temporal, and spectral features from EEG signals, enhancing the model’s ability to capture intricate patterns associated with emotional states. Experimental evaluations on the SEED-IV dataset, encompassing four emotional categories—Neutral, Happy, Sad, and Fear—demonstrated the model’s exceptional performance, achieving a macro-average F1-score of 93% and an area under the ROC curve (AUC) of 0.94. These results validate the model’s effectiveness in accurately distinguishing complex emotional patterns, even under noisy conditions and inter-class ambiguities. Overall, this research advances the domain of EEG-based emotion recognition by introducing a high-performing, interpretable framework suitable for real-world applications while laying the foundation for future developments in adaptive neurofeedback systems and emotion-aware brain-computer interfaces.
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