Emotion recognition is pivotal in numerous fields like psychology, human–computer interaction, and affective computing. Traditionally, methods for this task relied on explicit feature engineering and classification algorithms, which often struggled with the complexity and variability of human emotions. Recently, deep learning has emerged as a powerful alternative, capable of automatically learning intricate patterns from raw data, offering promising advancements in emotion recognition tasks. This study delves into applying deep learning techniques to analyze EEG signals for emotion recognition. It explores various deep learning architectures such as Convolutional Neural Networks (CNNs) and Hybrid model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models. Furthermore, the study investigates attention mechanisms and transfer learning strategies to enhance the models’ ability to discern relevant features and generalize across different datasets and domains. Experimental evaluations are conducted using the SEED-V emotion dataset to assess the performance of the proposed deep learning models in terms of accuracy, robustness, and efficiency compared to existing methods. According to our findings, the CNN model achieved an emotion classification accuracy of 81%, while the Hybrid CNN-LSTM model demonstrated an accuracy of 83%, highlighting its effectiveness in emotion recognition tasks.