Micro Expression with EEG and Face Emotion: Challenges and Opportunities

Emotion recognition through Brain-Computer Interface (BCI) technology has gained considerable attention due to its potential applications in human-computer interaction, healthcare, and entertainment. Electroencephalography (EEG) plays a pivotal role in capturing real-time brain activity associated with emotional states, providing insights into both cognitive and affective responses. This review paper presents a comprehensive analysis of EEG-based emotion recognition research from 2014 to 2024, emphasizing the role of both micro and macro ex- pressions in decoding emotional nuances. Micro-expressions, which are brief and involuntary, offer a window into subtle emotional changes, while macro-expressions reveal more overt emotional reactions. By integrating EEG signals with advanced facial analysis methods, particularly in scenarios with occluded or ambiguous facial cues, this paper explores the methodologies, datasets, and feature extraction techniques used to classify emotions in real time. Key challenges and advancements are dis- cussed, including issues related to data variability, classification accuracy, and the integration of BCI systems with emotion recognition frame- works. This review aims to identify emerging trends and propose future research directions to enhance the precision and applicability of emotion recognition technologies in BCI contexts.