NeuroCogniCaps: Unveiling Emotional Landscapes with Advanced Capsule Networks in EEG-Based Emotion Recognition

Emotion recognition from EEG signals is a critical aspect of enhancing human-computer interaction across various domains, such as healthcare and entertainment. This research paper introduces an innovative approach to emotion recognition by harnessing the power of Capsule Networks, an advanced deep learning architecture. The study focuses on effectively capturing intricate patterns in EEG data associated with different emotions, presenting a robust method for classifying emotions into three distinct categories: NEGATIVE, NEUTRAL, and POSITIVE. The proposed Capsule Network model showcases promising results in the realm of EEG-based emotion recognition, emphasizing its potential to contribute significantly to the field. Through a comprehensive analysis of experimental outcomes, this paper highlights the effectiveness of the Capsule Network architecture in discerning and classifying diverse emotional states. The implications of this research extend to applications in human-computer interaction, paving the way for advancements in healthcare, entertainment, and beyond.