The clinical application of brain-computer interface technology accelerates, with multiple experts decoding implementation pathways.

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Source: Yicai Finance

Currently, with continuous breakthroughs in brain-computer interface technology, related diagnostic and treatment facilities and service levels are becoming hot topics among industry insiders.

At the first Shanghai Blueborn Brain Science and Brain-Computer Interface International Forum (hereinafter referred to as “the Forum”) held on the 14th, many domestic and international brain science experts discussed specific applications of brain-computer interface technology; they believe that brain-computer interface technology has benefited patients in disease diagnosis and treatment, brain function regulation, and neural awakening. The next key direction is how to deeply integrate with AI and other digital tools and analyze neural information from multiple dimensions.

From research to application

Academician of the Chinese Academy of Sciences and former president of Tongji University, Pei Gang, stated at the forum that currently, brain-computer interfaces are expected to reshape the paradigm of neurological disease diagnosis and treatment. However, this urgently requires strengthening the deep integration of basic research and clinical translation. “We should streamline the innovation chain from research to application and further connect hospitals with society, doctors with patients, to address urgent clinical needs.”

Another important issue is how to truly turn brain-computer interface technology into standardized, implementable clinical solutions.

Yicai learned that in January 2026, the “Chinese Expert Consensus on the Management of Clinical Application Pathways for Implantable Brain-Computer Interfaces” organized by the Neuro Neurosurgery Branch of the Chinese Medical Association and written by clinical neuro medicine experts was officially released. The consensus clarifies the application pathway and complete process of implantable brain-computer interfaces (iBCI) in clinical settings, and designs adverse event handling plans and exit mechanisms.

“Applying iBCI technology in clinical practice requires designing a standardized management approach to handle many aspects, including ‘indications classification for clinical application,’ ‘inclusion and exclusion criteria for subjects,’ and ‘clinical application pathways throughout the entire cycle,’” said Yang Yi, one of the draft team members and executive deputy director of the Brain-Computer Interface Translational Research Center at the National Center for Neurological Diseases.

Yang Yi stated that clinical application of iBCI needs to develop specific preoperative assessments, surgical implantation, postoperative training, and long-term follow-up plans for patients with spinal cord injury, stroke, ALS (amyotrophic lateral sclerosis), and others. Additionally, personalized electrode selection, multimodal imaging localization, and closed-loop rehabilitation training are also crucial. “Behind research and application of brain-computer interfaces, there should be more standardized medical guarantees to facilitate neural function reconstruction for patients with neurological disabilities.”

Dr. Zhu Guoxing, director of neurology at Huashan Hospital affiliated with Fudan University, said that the main value of brain-computer interface technology in current clinical applications is “medical rehabilitation,” especially helping high-level paralysis patients control robotic arms, exoskeletons, etc., to achieve autonomous eating and movement. The core function behind this is the collection and analysis of EEG signals.

Zhu Guoxing further noted that mainstream EEG (Electroencephalogram) electrode-controlled brain-computer interface devices still have some shortcomings, such as low signal-to-noise ratio and interference from artifacts, difficulty decoding complex intentions, and a lack of universal EEG signal decoding large models in the industry.

He indicated that in the future, electrodes modified with nanomaterials and flexible, stretchable electrodes may become new directions in brain-computer interface device development. Besides clinical applications, non-implantable brain-computer interface devices can also analyze EEG signal changes to support scenarios like cognitive science and sleep enhancement.

Neural awakening is also becoming another major focus in clinical applications. Lu Yunhe, deputy director of neurosurgery at Tongji Hospital affiliated with Tongji University, cited a clinical trial example: for 46 patients with persistent consciousness disorders, a systematic, stepwise treatment strategy (including cerebrospinal fluid management, spinal cord electrical stimulation to regulate neural circuits, and closed-loop functional compensation) resulted in a consciousness improvement rate of 67.4% over a 12-month follow-up. “This could provide a new precise clinical pathway for patients with long-term consciousness impairment.”

How AI Empowers

The integration of AI and brain-computer interfaces is showing enormous application potential.

Jin Jing, deputy dean of the School of Mathematics at East China University of Science and Technology, believes that currently, brain-computer interfaces face issues such as limited functions (different interaction objects require different systems), restricted paradigms (relying on pre-set commands and layouts), and low intelligence (unable to perform more complex tasks).

Jin Jing suggests that AI large language models should be embedded into BCI operation, establishing scene-specific brain-computer interaction modules to make BCIs more flexible and practical. Additionally, deep integration with mixed reality can create a virtual-physical combined control framework, providing a stable foundation for mobile BCIs.

Zhao Cunzhong, head of operation at Alibaba Qianwen Health, believes that AI participation can enhance the dynamic optimization of neural signal decoding and intervention plans in brain-computer devices or products. Meanwhile, brain-computer interfaces provide AI with precise neural data and intervention pathways, extending health management from “physiological indicators on the body surface” to “precise regulation at the central nervous system level,” enabling a deep upgrade of health management.

For example, in neurological rehabilitation, AI-enabled brain-computer devices can transmit decoded movement intentions to assistive devices like exoskeletons, enabling limb movement functions, restoring upper limb mobility, and recording training data to evaluate rehabilitation effects, forming a “training-evaluation-adjustment” closed loop.

Another example is in neurological disease management: AI can dynamically perceive pathological neural signals through closed-loop brain-computer interfaces in diseases like Parkinson’s, automatically adjusting deep brain stimulation (DBS) parameters to precisely suppress symptoms like tremors and bradykinesia, improving intervention effectiveness," Zhao said.

Fernando Goldenberg, co-director of the Stroke Center at the University of Chicago Medical Center, proposed using AI to drive clinical decision-making in neuroscience, including predicting hypoxic-ischemic brain injury (HIBI) after cardiac arrest, spontaneous intracerebral hemorrhage (ICH) prediction, and deriving key conclusions for blood pressure management and PBI (penetrating brain injury) scoring. “The core value of these imaging-based predictive models lies in converting images into time-series data rather than single reports, helping doctors identify worsening conditions earlier and better match treatment with disease progression.”