Spatio-temporal fusion of fNIRS signals with multi-view structured sparse canonical correlation analysis for depression detection

Multi-view learning is a rapidly developing field that enables comprehensive data analysis from multiple perspectives. Despite its potential, it has been rarely applied to depression detection using functional near-infrared spectroscopy (fNIRS). In this study, we propose a novel fusion algorithm, named multi-view structured sparse canonical correlation analysis (MS²CCA). It combines the ℓ_2,1 norm and fused group lasso regularization to suppress noise and maintain local smoothness. It also reveals the underlying group structure among features and produces clearer canonical weight patterns. Our experiments involve 60 individuals with depression and 60 healthy controls. MS²CCA achieves an accuracy of 86.31 %, a precision of 86.96 %, a recall of 87.71 %, and an F1-score of 0.87. Compared to state-of-the-art algorithms, it outperforms by more than 4.7 % across all metrics. These results demonstrate that MS²CCA facilitates more reliable detection outcomes and holds promise for future applications in clinical auxiliary diagnosis.