Background: Non-invasive electroencephalography (EEG)-based motor imagery brain-computer interfaces (MI-BCIs) offer promising neurorehabilitation pathways by decoding imagined movements to drive assistive rehabilitation devices. However, translating these systems from controlled laboratories into active clinical workflows remains a significant hurdle.
Objective: This narrative review examines the socio-technical, computational, and practical challenges of deploying MI-BCIs for clinical rehabilitation, with a specific focus on the fragmented, high-throughput healthcare sector in India.
Review Methodology: A structured search was conducted across international and national databases—including PubMed, Scopus, Google Scholar, and the Clinical Trials Registry - India (CTRI)—identifying 15 key studies published between 2012 and 2026 focusing on human-machine co-adaptation, user training standardization, and real-world BCI usability.
Conclusion: By synthesizing advanced computational methods—such as real-time adaptive classifiers and Markov chain-based user assessments—with ground-level clinical workflows, this review outlines a four-stage co-adaptive translational blueprint. We advocate for a paradigm shift from pure algorithmic decoding accuracy to holistic, patient-centered usability, establishing a sustainable framework for scalable clinical neurorehabilitation in emerging economies.
Brain-computer interface, BCI research, clinical BCI, neurorehabilitation, BCI deployment, motor imagery, rehabilitation technology, BCI usability