Adaptive deep canonical correlation analysis–based multimodal sentiment analysis

Abstract

Emotion recognition plays a critical role in affective computing and human-computer interaction. While physiological signals such as electroencephalography (EEG) and eye-tracking offer valuable insights into emotional states, effectively fusing these heterogeneous modalities remains challenging due to differences in temporal scale, dimensionality, and signal characteristics. Traditional fusion methods employ fixed strategies that fail to adapt to dynamic changes in modality reliability and cross-subject variability, limiting their practical applicability.