Recent breakthroughs in brain-computer interface (BCI) technology have drawn industry attention to advancements in diagnostic facilities and service capabilities. At the inaugural Shanghai Lansheng International Forum on Brain Science and Brain-Computer Interface, experts discussed practical applications of BCI technology. They highlighted its benefits in disease diagnosis and treatment, brain function regulation, and neurostimulation for consciousness recovery. A key future direction involves deeper integration with digital tools like artificial intelligence and multi-dimensional analysis of neural information.
Transitioning from research to application, an academician noted that BCI technology has the potential to reshape treatment paradigms for neurological diseases. This requires stronger integration between basic research and clinical translation, establishing an innovation chain from research to practical use, and improving connections between hospitals, society, doctors, and patients to address urgent patient needs.
A critical challenge is standardizing BCI technology into actionable clinical protocols. In January 2026, a Chinese expert consensus on the clinical application pathway management of implantable BCI was released. This document outlines application pathways, full-process procedures, adverse event management, and exit mechanisms. One drafter emphasized that standardized management is essential for clinical application, covering areas such as indication classification, participant inclusion and exclusion criteria, and full-cycle clinical pathways.
Clinical use of implantable BCI requires tailored preoperative assessments, surgical implantation, postoperative training, and long-term follow-up plans for patients with spinal cord injuries, stroke, or ALS. Individualized electrode selection, multi-modal imaging, and closed-loop rehabilitation training are also vital. The goal is to provide standardized medical support for functional recovery in patients with neurological impairments.
A chief physician pointed out that the current primary value of BCI in clinical settings is medical rehabilitation, particularly helping patients with high-level paralysis achieve autonomy through controlling robotic arms or exoskeletons. Core to this is the collection and analysis of EEG signals. However, mainstream EEG-based BCI devices face limitations such as low signal-to-noise ratio, artifact interference, difficulty decoding complex intentions, and a lack of universal EEG decoding models.
Future development may focus on electrodes modified with nanomaterials or flexible, stretchable electrodes. Non-invasive BCI devices could also support cognitive science and sleep improvement through EEG analysis. Another emerging application is neurostimulation for consciousness recovery. A clinical trial involving 46 patients with persistent consciousness disorders showed a 67.4% improvement rate after 12 months using a systematic, stepwise therapy strategy.
The integration of AI and BCI shows significant potential. Current BCI systems face issues like limited functionality, reliance on pre-set commands, and low intelligence. Incorporating large language models could enhance flexibility and practicality, while mixed reality integration could provide a stable framework for mobile BCI applications.
AI can improve BCI's ability to dynamically optimize neural signal decoding and intervention plans. Conversely, BCI offers AI precise neural data and intervention pathways, extending health management from surface-level physiological indicators to central nervous system regulation. For example, in neurorehabilitation, AI-enhanced BCI devices can decode movement intent and assist with limb functional recovery through exoskeletons, creating a closed-loop system for training, evaluation, and adjustment.
In neurological disease management, AI can use closed-loop BCI to dynamically monitor pathological signals and adjust deep brain stimulation parameters for conditions like Parkinson's disease, improving symptom control. One expert also highlighted AI's role in clinical decision-making for conditions such as hypoxic-ischemic brain injury and spontaneous intracerebral hemorrhage, where predictive models convert imaging data into time-series information for earlier detection and tailored treatment.
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