A Passive fNIRS-Based Brain–Computer Interface for Differentiating Large Vessel Occlusion from Non-LVO Ischemic Stroke

Abstract

Rapid identification of large vessel occlusion (LVO) is essential for timely thrombectomy; yet, definitive diagnosis requires vascular imaging that is unavailable in many prehospital settings. We investigated whether resting-state functional near-infrared spectroscopy (fNIRS) captures spatially organized signals associated with LVO and can support task-free classification within a passive brain–computer interface framework. Seventy-seven patients with suspected stroke were enrolled, and 47 met prespecified quality-control and diagnostic criteria (17 LVO and 30 non-LVO ischemic stroke). We compared channel-level and anatomically grouped regional features using age- and sex-adjusted multivariate analyses and participant-level cross-validation. Channel-level differences did not remain significant after adjustment, whereas regional multivariate effects persisted, particularly in sensorimotor regions. The regional time-domain model achieved 82.4% accuracy, 76.2% sensitivity, 86.0% specificity, and an area under the receiver operating characteristic curve of 0.832 in internal fixed-configuration cross-validation. Predictive contributions were distributed across all four predefined regions, while adding feature families through early or late fusion did not improve performance. These convergent statistical and classification results indicate that LVO-associated fNIRS information is spatially distributed and is captured more effectively by anatomical aggregation than by isolated-channel analysis or greater feature complexity. The findings support multicenter evaluation of task-free fNIRS for early LVO risk assessment.