A Novel Transfer Learning‐Based Hybrid EEG‐fNIRS Brain‐Computer Interface for Intracerebral Hemorrhage Rehabilitation

The study has developed the first clinically validated hybrid EEG‐fNIRS brain‐computer interface fusion framework that systematically overcomes cross‐subject generalization barriers through a novel Wasserstein metric‐driven neural template selection mechanism. By quantitatively mapping neural distribution divergences between 17 normal controls and 13 intracerebral hemorrhage patients during motor imagery tasks, this multimodal system achieves 74.87% patient‐specific classification accuracy via optimal normal template transfer. Abstract Motor imagery (MI)‐based neurorehabilitation shows promise for intracerebral hemorrhage (ICH) recovery, yet conventional unimodal brain‐computer interfaces (BCIs) face critical limitations in cross‐subject generalization. This study presents a multimodal electroencephalography (EEG)‐functional near‐infrared spectroscopy (fNIRS) fusion framework incorporating a Wasserstein metric‐driven source domain selection method that quantifies inter‐subject neural distribution divergence. Through comparative neuroactivation analysis of 17 normal controls and 13 ICH patients during MI tasks, the transfer learning model achieved 74.87% mean classification accuracy on patient data when trained with optimally selected normal templates. Cross‐validation on two public hybrid EEG‐fNIRS datasets demonstrated generalizability, increasing baseline accuracy to 82.30% and 87.24%, respectively. The proposed system synergistically combines the millisecond temporal resolution of EEG with the hemodynamic spatial specificity of fNIRS, establishing the first clinically viable multimodal analytical protocol for ICH rehabilitation. This paradigm advances neurotechnology translation by paving the way for personalized rehabilitation regimens through robust cross‐subject neural pattern transfer while addressing the critical barrier of neurophysiological heterogeneity in post‐ICH populations. The study has developed the first clinically validated hybrid EEG-fNIRS brain-computer interface fusion framework that systematically overcomes cross-subject generalization barriers through a novel Wasserstein metric-driven neural template selection mechanism. By quantitatively mapping neural distribution divergences between 17 normal controls and 13 intracerebral hemorrhage patients during motor imagery tasks, this multimodal system achieves 74.87% patient-specific classification accuracy via optimal normal template transfer. Abstract Motor imagery (MI)-based neurorehabilitation shows promise for intracerebral hemorrhage (ICH) recovery, yet conventional unimodal brain-computer interfaces (BCIs) face critical limitations in cross-subject generalization. This study presents a multimodal electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) fusion framework incorporating a Wasserstein metric-driven source domain selection method that quantifies inter-subject neural distribution divergence. Through comparative neuroactivation analysis of 17 normal controls and 13 ICH patients during MI tasks, the transfer learning model achieved 74.87% mean classification accuracy on patient data when trained with optimally selected normal templates. Cross-validation on two public hybrid EEG-fNIRS datasets demonstrated generalizability, increasing baseline accuracy to 82.30% and 87.24%, respectively. The proposed system synergistically combines the millisecond temporal resolution of EEG with the hemodynamic spatial specificity of fNIRS, establishing the first clinically viable multimodal analytical protocol for ICH rehabilitation. This paradigm advances neurotechnology translation by paving the way for personalized rehabilitation regimens through robust cross-subject neural pattern transfer while addressing the critical barrier of neurophysiological heterogeneity in post-ICH populations. Advanced Science, Volume 12, Issue 43, November 20, 2025.