fNIRS emotion recognition method based on multi-scale fusion features

Functional near-infrared imaging is a brain imaging technology that measures changes in cerebral blood oxygenation. Due to its high portability and spatial resolution, fNIRS is widely employed in emotion cognition. However, current feature extraction methods for fNIRS predominantly rely on statistical feature engineering, often lacking deeper exploration and analysis of both long-term and short-term characteristics of fNIRS signals. In response to this limitation, this paper presents research conducted on the fNIRS emotion dataset NEMO and introduces an emotion recognition method based on multi-scale fusion features. By integrating vector features and time-delay embedding features, the proposed method facilitates the identification of dimensional emotions. The model demonstrates its efficacy by effectively distinguishing emotions in binary classifications of valence and arousal, as well as in the four-class classification of dimensional emotions. These results confirm the utility of fNIRS in emotion decoding and emotional state assessment, offering valuable blood oxygen signal features for emotion recognition. Furthermore, this work provides a viable approach for decoding the neural activities associated with dimensional emotions, and introduces a new perspective for advancing our understanding of brain processes related to emotion.