Locality-constrained robust discriminant non-negative matrix factorization for depression detection: An fNIRS study

Abstract

Major depressive disorder (MDD) is having an increasingly severe impact worldwide, which creates a pressing need for an efficient and objective method of depression detection. Functional near-infrared spectroscopy (fNIRS), which directly monitors changes in cerebral oxygenation, has become an important tool in depression research. Currently, feature extraction methods based on multi-channel fNIRS data often overlook the local structure of the data and the subsequent classification cost. To address these challenges, we introduce an innovative feature extraction algorithm, namely locality-constrained robust discriminant non-negative matrix factorization (LRDNMF). The algorithm incorporates ${\ell }_{2,1}$ regularization, local coordinate constraints, within-class scatter distance, and total scatter distance, achieving a fusion of robustness, locality, and discrimination. LRDNMF enhances feature representation, reduces noise impact, and significantly boosts classification ability. Based on experimental results from 56 participants, LRDNMF achieves an accuracy of 90.55%, a recall of 91.48%, a precision of 90.46%, and an F1 score of 0.91 under full stimuli. These results outperform existing algorithms, validating the effectiveness of LRDNMF and demonstrating its significant potential in auxiliary diagnosis of depression.