Cross-device reproducibility of fNIRS biomarkers in SLE via EEG spatial mapping: a dual-site study

To develop an EEG spatial prior mapping-based weighted common spatial pattern-support vector machine (MPW-CSP-SVM) framework and evaluate within-site classification, cross-device reproducibility of independent spatial findings, and associations with clinical measures across two fNIRS datasets. fNIRS signals were acquired during a verbal fluency task from Site 1 (77 SLE, 54 controls, 37 channels) and Site 2 (75 SLE, 38 controls, 50 channels). Native fNIRS channels were mapped to EEG-coordinate targets using fixed site-specific operators: equal-weight regional averaging at Site 1 and inverse-distance-squared weighting at Site 2. CSP (m = 4) extracted eight spatial features, followed by linear SVM classification. Performance was evaluated via repeated stratified cross-validation. Key regions were identified by permutation and bootstrap tests. Correlations with the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), Hamilton Depression Rating Scale (HAMD), and Hamilton Anxiety Rating Scale (HAMA) were analyzed. MPW-CSP-SVM achieved AUCs of 0.786 ± 0.013 (Site 1) and 0.839 ± 0.004 (Site 2), significantly outperforming raw-feature SVM, fusion-feature SVM, and a few-shot Transformer (all p < 0.001). Consistent key regions emerged in right frontal (F4) and right frontotemporal (F8/FT8) areas. CSP features correlated negatively with SLEDAI (up to r = - 0.197) and HAMD (r = - 0.255 at Site 1). A five-feature model explained 24.4% of SLEDAI variance (p < 0.001). MPW-CSP-SVM consistently outperformed the comparator models at both sites and identified convergent right frontal and frontotemporal contributions despite differences in fNIRS devices and montages. Associations between selected CSP features and SLEDAI support their further evaluation as candidate task-related functional markers in SLE.

The online version contains supplementary material available at https://doi.org/10.1007/s11571-026-10549-6.