Bridging Temporal and Cepstral Hemodynamic Intelligence: An Adaptive Multi-Domain Feature Fusion Framework for Multi-Class fNIRS Brain–Computer Interfaces

Reliable four-class decoding remains challenging for functional near-infrared spectroscopy (fNIRS) brain–computer interfaces (BCIs), because a single feature domain captures only part of the hemodynamic response. This study proposes an Adaptive Multi-Domain Feature Fusion (AMFF) framework that combines six temporal hemodynamic descriptors with ten fNIRS-adapted Mel-frequency cepstral coefficients (MFCCs) and learns sample-dependent weights for the two domains before classification with a multilayer perceptron. The framework was evaluated on an open-access dataset of 29 participants performing left- and right-hand motor imagery, mental arithmetic, and rest, using subject-independent leave-one-subject-out cross-validation. AMFF achieves a mean accuracy of 95.6%, with precision, recall, and F1-score of 94.6%, 95.9%, and 95.3%, respectively, outperforming temporal only (90.7%), cepstral-only (95.0%), and direct concatenation (95.2%) representations, with a modest but consistent gain over direct concatenation across participants. Friedman and Holm-corrected Wilcoxon signed-rank tests confirmed significant differences for all four metrics; AMFF exceeded static concatenation for every participant, although by a small margin (0.2–0.7 percentage points). Most of the gain over temporal descriptors therefore comes from the cepstral domain, with adaptive weighting adding a small but consistent refinement. MFCC parameter-sensitivity, coefficient-ablation, and descriptor-ablation analyses further characterize the contribution of each domain.