EEG-Based Emotion Recognition Using CNN-LSTM: Dynamic Segmentation and Feature Fusion

This study examines current developments and persistent difficulties in identifying emotions from EEG data, particularly when it comes to real-time systems. The need for precise, quick-response models has increased as interest in emotion-aware applications—from adaptive human-computer interfaces to mental health tools—increases. Although deep learning methods such as CNNs and LSTMs have demonstrated remarkable accuracy (up to 98%), a number of practical issues still need to be addressed, especially in the areas of delay minimization and data preprocessing. In order to improve recognition speed and reliability, the research presents real-time prioritization techniques and dynamic segmentation procedures. It also examines the wider socioeconomic and ethical implications of EEG-based systems and highlights important avenues for further study, such as multimodal feature fusion and dataset diversification.