Transfer Learning for Emotion Recognition Across Depression Patients and Healthy Subjects with Data Alignment and Selection

Identifying and understanding emotions is crucial, particularly when considering a range of subjects. This study introduces a novel approach to emotion recognition between depression patients and healthy subjects, amalgamating data alignment (DA) and subject selection (SS) within the transfer learning framework. Three methodologies for DA are explored, including Riemannian alignment (RA), Euclidean alignment (EA), and correlation alignment (CORAL). The Jensen -Shannon (JS) divergence is employed to gauge the similarity between target and source subjects to select potential training datasets. Through conducting cross-subject experiments within depression and healthy cohorts and employing diverse models, experimental outcomes evince notable enhancements in recognition efficacy facilitated by this combined DA and SS transfer learning paradigm. Moreover, the study demonstrates that despite cognitive challenges in emotion recognition among individuals with depression and those without the disorder, skillful design enables the utilization of data from healthy individuals and trained algorithms to greatly enhance emotion recognition in depression patients, resulting in significant benefits.