NSFC Launches 2026 Brain-Computer Interface Joint Fund with 2.4 Million RMB per Key Project

Brain-Computer Interface Field

Key Supported Projects

Shanghai Shuli Intelligent Technology Co.,Ltd

1. Research on the Mechanism of Brain-Computer Interface-Mediated Functional Remodeling for Central Motor Impairment (Select a subordinate code under H09 for Application Code 1)

Brain-computer interfaces (BCIs) face key clinical challenges in the treatment of neural injuries, including uncertain therapeutic efficacy and a lack of evidence-based guidance for personalized treatment. By collaborating with multiple clinical centers and implementing standardized data collection, we aim to construct a high-quality, highly consistent, large-scale, multidimensional real-world database. We will investigate the response patterns and neural remodeling mechanisms of different patient subgroups to BCI interventions, develop personalized efficacy prediction models, and establish evidence-based validation of therapeutic outcomes.

2. Analysis of Abnormal Brain Network Mechanisms in Pediatric Attention-Deficit/Hyperactivity Disorder and Research on Digital Therapeutics (Select a subordinate code under H09 for Application Code 1)

The neural mechanisms underlying pediatric attention-deficit/hyperactivity disorder (ADHD) remain unclear, and there is a lack of objective assessment tools and precise intervention methods. We will establish a multicenter prospective ADHD cohort, integrate multimodal data such as fMRI and EEG, and employ brain normotyping and machine learning approaches to elucidate the mechanisms of brain network abnormalities and identify neuromodulation targets for personalized interventions. Furthermore, we will develop convenient assessment tools based on neural biomarkers and create an integrated closed-loop intervention system combining precise neuromodulation, neurofeedback, and cognitive training, followed by preliminary clinical validation.

3. Elucidation of the Mechanisms Underlying Multimodal Brain Network Abnormalities, Objective Assessment, and Digital Therapeutics in Pediatric Autism Spectrum Disorder (Select a subordinate code under H09 for Application Code 1)

The core neural mechanisms underlying social deficits in children with autism spectrum disorder (ASD) remain unclear, and there is a lack of objective assessment tools and targeted intervention strategies. This study aims to establish a multicenter cohort, integrate multimodal neuroimaging with clinical data, elucidate the mechanisms of social brain network abnormalities in ASD, and identify key targets for intervention. We will develop a closed-loop brain-computer interface (BCI) intervention system based on individualized neural targets and validate its efficacy through clinical trials.

4. Research on the Neural Mechanisms and Diagnostic Systems of Primary Immunodeficiency Affecting Brain Development in Children (Select a Subcode under H11 for Application Code 1)

Elucidate the neural mechanisms by which primary immunodeficiency affects pediatric brain development and develop early diagnostic tools. By establishing a multicenter prospective cohort comprising children with primary immunodeficiency and typically developing children, and integrating multimodal neuroimaging and clinical data, systematically analyze the association between abnormal immune indicators and deviations in brain developmental trajectories, along with their key pathways. Based on these findings, leverage artificial intelligence technologies to mine early biomarkers and construct diagnostic and prognostic prediction models, thereby achieving clinical validation of the “mechanistic elucidation–early diagnosis” framework.

5. Research on the Mechanisms and Early Intervention Strategies for Neurodevelopmental Disorders and Neurodegenerative Diseases Caused by Immune Dysfunction Based on Brain-Computer Interfaces (Select a Subcode under H09 for Application Code 1)

By constructing a multimodal cohort that integrates clinical data from immunology, neuroimaging, and electroencephalography (EEG), this study investigates the synergistic mechanisms underlying neurodevelopmental and neurodegenerative disorders caused by immune dysfunction. It leverages artificial intelligence to mine key immune-related biomarkers and build early risk warning models. Furthermore, it aims to develop portable screening tools based on brain-computer interfaces (BCI) and design closed-loop intervention strategies targeting immune-related neural circuit abnormalities. Ultimately, this work seeks to establish an early identification and treatment framework encompassing “immune mechanism elucidation, early warning, and targeted intervention.”

6. Research on Multimodal Neural Decoding and Closed-Loop Non-Pharmacological Modulation of Acute Visceral Pain Induced by Endoscopic Procedures (Select a subordinate code under H28 for Application Code 1)

For acute visceral pain induced by endoscopic procedures, this study investigates the dynamic mechanistic links among pain generation, analgesic interventions, and changes in brain physiological signals, focusing on the modulatory effects of non-pharmacological analgesic measures on pain-related cranial neural activity. A multicenter clinical cohort will be established to create a paired database correlating subjective pain perceptions with objective changes in brain physiological signals. Artificial intelligence methods will be employed to mine neural biomarkers and develop a real-time pain state decoding model suitable for bedside application. Based on the multimodal database and the real-time decoding model, a closed-loop brain-computer interface (BCI) modulation system integrating non-pharmacological regulatory intervention strategies will be developed and clinically validated.

7. Research on the Neuromodulatory Mechanisms of Synchronously Driven Exoskeleton and Temporal Spinal Cord Electrical Stimulation Based on Motor Imagery Decoding for the Treatment of Post-Stroke Limb Motor Dysfunction (Select a subordinate code under H09 for Application Code 1)

Investigating the Use of Spinal Cord Stimulation to Induce Spike-Timing-Dependent Neural Plasticity for Precise Intervention in Chronic Post-Stroke Motor Dysfunction. This study will establish personalized motor intention decoding models and drive hand exoskeletons through real-time decoding, while simultaneously delivering precisely timed spinal cord stimulation to create a personalized therapeutic paradigm. The safety, feasibility, and preliminary efficacy of this closed-loop treatment protocol will be systematically evaluated to validate its effectiveness in promoting functional recovery by inducing neural plasticity, thereby providing evidence for the development of next-generation precise neuromodulation therapies.

8. Research on Non-Invasive Active Nursing Rehabilitation Systems for Severe Motor Dysfunction (Select a Sub-code under F03 for Application Code 1)

To address the poor robustness of motor intent decoding and unnatural control interaction in non-invasive brain-computer interfaces (BCIs) for severely paralyzed patients in real-world, dynamic daily environments, we are developing high-robustness decoding algorithms capable of stable operation under complex daily disturbances. This enables precise recognition of intents for “grasping” and “care-related position changes.” By integrating functional electrical stimulation (FES) with smart devices, we achieve low-latency, coordinated control, supporting natural, active functional substitution and autonomous position management. We aim to construct and validate a non-invasive active nursing and rehabilitation prototype system tailored for severe motor dysfunction, providing patients with proactive rehabilitation and assistive solutions.

9. Research on the Interaction Mechanisms of Multi-Organ Perfusion Dysfunction in the Heart and Brain Caused by Pan-Vascular Diseases and Interventional Regulation via Vagus Nerve Stimulation (Select a Sub-code under H09 for Application Code 1)

Addressing the issue of multi-organ perfusion dysfunction, particularly in the heart and brain, caused by pan-vascular diseases, this study aims to construct clinical cohorts through standardized prospective data collection. By systematically analyzing multimodal data—including fused signal characteristics such as electroencephalogram (EEG) and electrocardiogram (ECG), as well as radiomics—we seek to elucidate common pathophysiological mechanisms. Furthermore, we will explore the impact of neuromodulation techniques, such as vagus nerve stimulation, on multi-organ perfusion function in pan-vascular diseases, thereby providing a theoretical basis for precise diagnosis and concurrent treatment.

10. Elucidation of the Pathological Mechanisms of Obstructive Sleep Apnea Driven by the Gut–Brain–Respiratory Axis and Research on Multimodal Precision Intervention Technologies (Select a subordinate code under H01 for Application Code 1)

To address the challenges of unclear mechanisms and limited interventions for obstructive sleep apnea (OSA), this study elucidates the pathogenic mechanisms of OSA and develops precision intervention technologies from the systemic perspective of the “gut–brain–respiration axis.” By establishing a multicenter cohort and integrating multimodal data, including gut microbiota and electroencephalography (EEG), we aim to decipher the underlying pathological mechanisms. We will develop brain-computer interface (BCI)-based algorithms for respiratory event warning and monitoring prototypes. Furthermore, through randomized controlled trials, we will validate the synergistic therapeutic effect of fecal microbiota transplantation combined with neuromodulation, comparing it with classical detection and treatment methods, thereby constructing a system for individualized precision phenotyping and intervention.

11. Research on the Construction of Multicenter Standardized Datasets for Typical Clinical Application Scenarios of Non-Invasive Brain-Computer Interfaces (Select a subordinate code under F06 for Application Code 1)

Addressing the critical lack of high-quality, standardized multi-center datasets in the clinical application and algorithm development of non-invasive brain-computer interfaces (BCIs), this initiative targets typical scenarios such as stroke rehabilitation and assessment of disorders of consciousness. By collaborating with multiple clinical centers, we have developed a standardized end-to-end data acquisition methodology and established a multi-center collaborative network to enable large-scale collection of multi-paradigm, multimodal clinical data. This effort aims to construct an open, shared, multi-center standardized clinical dataset that covers typical application scenarios and complies with international standards. Furthermore, we have established supporting specifications for data management, cleaning, quality control, and sharing, thereby providing a robust data foundation for reliable algorithm training and clinical validation.

12. Research on Risk Stratification and Closed-Loop Intervention for Self-Injury in Children and Adolescents with Treatment-Resistant Mood Disorders Based on Non-Invasive Brain-Computer Interfaces (Select a subordinate code under F02 for Application Code 1)

To address the challenges of insufficient objective quantification of self-harm risk, difficulties in identifying changes in risk status, and the lack of personalized adjunctive interventions for treatment-resistant mood disorders in children and adolescents, we aim to establish a multimodal research cohort covering varying levels of self-harm risk. This initiative will construct a cross-modal database encompassing dimensions such as non-invasive electroencephalogram (EEG) signals, neuroimaging, peripheral molecular profiles, and clinical phenotypes. The project will investigate brain state perception, cross-modal decoding, and closed-loop feedback regulation methods driven by non-invasive brain-computer interfaces (BCIs). Furthermore, it will develop BCI decoding models, risk stratification and disease progression assessment methods, and closed-loop intervention technologies tailored to clinical heterogeneity, missing modalities, and high-noise scenarios, thereby providing new theoretical frameworks and methodologies for the stratified assessment, early warning, and personalized intervention of self-harm risk.

13. Research and Development of a New Generation of Subdural Brain-Computer Interface Electrode Systems for Clinical Applications and Early Clinical Validation Studies (Select a subordinate code under H28 for Application Code 1)

To address the clinical needs of implantable brain-computer interfaces (BCIs), we are developing a new-generation high-density flexible cortical electrode system with a subdural implantation interface. We are researching and developing novel highly compliant subdural electrode arrays that conform to the irregular anatomical structures of the subdural space, thereby addressing challenges related to adhesion and biocompatibility within confined spaces. Additionally, we are developing low-power, highly integrated wired/wireless acquisition modules to achieve miniaturization and stability of the system. Through large-animal implantation experiments, we will systematically evaluate surgical feasibility, short-term tissue compatibility, and signal recording stability. By obtaining preliminary in vivo neurophysiological data, we aim to validate electrode adhesion, biocompatibility, and recording stability, thereby providing key components and safety evidence for the early-stage clinical validation of intracranial BCIs.

(I) Applicant Eligibility.

The applicant shall meet the following conditions:

1. Possess experience in undertaking basic research projects or other engagements in basic research;

2. Hold a senior professional technical position (title);

Postdoctoral researchers currently stationed at a post, graduate students pursuing their degrees, and individuals who are unemployed or whose employer is not the supporting institution are ineligible to apply as applicants.

(II) Regulations on Application Limits.

Adhere to the relevant requirements for application limits specified in the “Application Regulations” of the Guidelines for National Natural Science Foundation of China Projects 2026.

For the 2026 cycle, applications to the Joint Fund for Innovative Development of Private Enterprises (Pilot) are not counted toward the total number of projects applied for and undertaken at the time of submission; they will be included in the count after formal acceptance. Researchers are limited to a total of one project, either applied for (including applicants and key participants) or currently undertaken (including principal investigators and key participants), under the Joint Fund for Innovative Development of Private Enterprises.

Applicants and their affiliated institutions shall carefully read and comply with the relevant requirements specified in this Project Guide, the “Guide to National Natural Science Foundation of China Projects for 2026,” and the “Notice on Matters Concerning the Application and Closure of National Natural Science Foundation of China Projects for 2026.”

1. This Joint Fund Project adopts a paperless application process. The submission period for applications is from July 5, 2026, to 16:00 on July 10, 2026.

2. This Joint Fund is open nationwide and operates on the basis of fair competition. Applicants are encouraged to conduct collaborative research with R&D institutions affiliated with the joint funding partners. For collaborative research projects, the application shall clearly specify the collaborative content and primary division of responsibilities among all parties involved. The number of participating institutions in key supported collaborative research projects shall not exceed three (the host institution plus two collaborating institutions), and the funding period is four years.

3. Applicants may apply for only one Joint Fund Project for the Innovative Development of Private Enterprises in the same year.

4. Applicants shall log in to the Network Information System of the National Natural Science Foundation of China (hereinafter referred to as the “Information System”) and prepare the application online. Applicants without an account in the Information System shall request their affiliated institution’s fund management contact to create one.

5. In the application form, select “Joint Fund Project” for the funding category, “Key Support Project” for the subcategory description, and “Private Enterprise Innovation Development Joint Fund” for the supplementary notes. “Application Code 1” should be selected in accordance with the guidelines of this joint fund project, while “Application Code 2” should be chosen based on the research field of the project. For “Field Information,” select the corresponding field name according to the research area, such as “Brain-Computer Interface Field.” For “Main Research Direction,” choose the appropriate direction name based on the project’s research focus, such as “1. Research on the Mechanisms of Functional Remodeling of Brain-Computer Interfaces for Central Motor Function Impairment.”

6. Applicants shall first state the name of the research direction specified in the project guide within the “Basis for Project Establishment and Research Content” section of the main text of the application.

7. The proposed project must fall within the funding scope and requirements outlined in this project guide. Applicants shall prepare the application in accordance with the outline for writing the project application. If the applicant is currently undertaking other national science and technology program projects related to this Joint Fund, the differences and connections between the proposed project and these related projects shall be discussed in the “Research Foundation and Working Conditions” section of the application body.

8. Research outcomes achieved through funded projects, including published papers, monographs, research reports, software, patents, awards, and media coverage of results, shall acknowledge support from the National Natural Science Foundation of China (NSFC) Joint Fund for Innovation and Development of Private Enterprises and cite the project approval number or provide relevant explanation. The National Natural Science Foundation of China and Shanghai Shuli Intelligent Technology Co., Ltd. jointly promote data sharing and the dissemination, application, and promotion of research outcomes.

9. The affiliated institution shall complete the required tasks, including issuing the Letter of Commitment from the Affiliated Institution, organizing applications, and reviewing application materials. It shall confirm and submit the electronic application form and supporting documents for the institution item by item through the information system no later than 16:00 on July 10, 2026.

Contact Information

Bureau of Planning and Policy, National Natural Science Foundation of China

Contact: Wang Xiaotian, Li Zhilan

Tel: 010-62328041, 62329897

Shanghai Shuli Intelligent Technology Co., Ltd.

Contacts: Fan Kunjun, Wang Hao

Tel: 021-61180122 (818), 61180122 (820)