Multimodal and Hybrid Brain-Computer Interfaces for Neurorehabilitation: From Signal Fusion to Clinical Translation

Brain-computer interfaces (BCIs) hold significant promise for neurorehabilitation, yet most current systems rely on a single neuroimaging modality—typically electroencephalography (EEG)—which inherently limits the richness and reliability of the captured neural information. Multimodal and hybrid BCI approaches, which integrate complementary signals such as EEG, functional near-infrared spectroscopy (fNIRS), electromyography (EMG), magnetoencephalography (MEG), and electrooculography (EOG), offer a pathway to overcome these constraints by improving signal quality, decoding robustness, and the range of neural and physiological features available for clinical decision-making.Despite growing interest, the clinical translation of multimodal BCIs remains in its early stages. Key open questions include how to optimally fuse heterogeneous signals with different temporal and spatial resolutions, how to design hybrid paradigms that are practical and tolerable for patient populations, and how to rigorously evaluate therapeutic efficacy beyond proof-of-concept demonstrations. Bridging these gaps requires close collaboration across neuroscience, biomedical engineering, rehabilitation medicine, and clinical trial methodology.This Research Topic aims to advance the development and clinical validation of multimodal and hybrid BCI systems for neurorehabilitation. We seek contributions that move beyond single-modality approaches and address the full pipeline from signal integration and cross-modal decoding to rehabilitation protocol design, clinical outcome assessment, and translational deployment.We welcome original research articles, systematic reviews, methods papers, and perspective articles addressing, but not limited to, the following themes:- Multimodal signal fusion strategies for BCI (e.g., EEG–fNIRS, EEG–EMG, EEG–MEG, and other combinations)- Cross-modal feature extraction and decoding methods for hybrid BCI systems- Design and evaluation of hybrid BCI paradigms tailored to clinical populations (e.g., stroke, spinal cord injury, disorders of consciousness)- Closed-loop neurofeedback and neuromodulation using multimodal BCI- Integration of BCI with digital therapeutics, robotic-assisted therapy, or functional electrical stimulation- Clinical trial design, efficacy endpoints, and patient stratification for BCI-based rehabilitation- Longitudinal clinical evidence on multimodal BCI interventions- Practical considerations for multimodal BCI deployment in clinical settings, including wearability, setup time, and patient compliance Topic editors: Yike Sun, Ying Shen, Shangen Zhang, Jingnan Sun, Tengyue Long Source: https://www.frontiersin.org/research-topics/82703/multimodal-and-hybrid-brain-computer-interfaces-for-neurorehabilitation-from-signal-fusion-to-clinical-translation