Emotion recognition via electroencephalography (EEG) has been gaining increasing attention in applications such as human–computer interaction, mental health assessment, and affective computing. However, it poses several challenges, primarily stemming from the complex and noisy nature of EEG signals. Commonly adopted strategies involve feature extraction and machine learning techniques, which often struggle to capture intricate emotional nuances and may require extensive handcrafted feature engineering. To address these limitations, we propose a novel approach utilizing convolutional neural networks (CNNs) for EEG emotion recognition. Unlike traditional methods, our CNN-based approach learns discriminative cues directly from raw EEG signals, bypassing the need for intricate feature engineering. This approach not only simplifies the preprocessing pipeline but also allows for the extraction of more informative features. We achieve state-of-the-art performance on benchmark emotion datasets, namely DEAP and SEED datasets, showcasing the superiority of our approach in capturing subtle emotional cues. In particular, accuracies of 96.32% and 92.54% were achieved on SEED and DEAP datasets, respectively. Further, our pipeline is robust against noise and artefact interference, enhancing its applicability in real-world scenarios.
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