Training‐Free Regulation of Grasping by Intracortical Tactile Feedback Designed via S1‐M1 Communication
Tactile feedback is essential for grip force control when operating a neuroprosthesis. Due to limited knowledge of cortical sensorimotor coordination, artificial feedback is mostly counterintuitive, requiring training to be associated with grasping behaviors. The current study investigates sensorimotor communication by recording neural activities from the primary sensory cortex (S1) and the primary motor cortex (M1) while macaques grasp targets of various textures and loads. Intracortical micro-stimulation is also delivered to S1 to validate the intervention of sensorimotor communication in grasping. The findings identify an S1→M1 functional pathway through which tactile information is transferred. The pathway is shared by both natural and artificial neural propagations. Moreover, it is demonstrated that sensory and motor decoding of neural activities in M1, as well as the actual grip force, are modulated by stimulation designed via S1→M1 communication, without prior training. The work provides a biomimetic strategy to design intuitive haptic feedback for brain-machine interfaces utilizing the S1→M1 pathway.
Authors
- Changyong Wang (ORCID: https://orcid.org/0000-0002-3350-8623)
- Z. Jane Wang (ORCID: https://orcid.org/0000-0002-3791-0249)
- Jin Zhou (ORCID: https://orcid.org/0000-0003-2447-3606)
- Xingyi Yang (ORCID: https://orcid.org/0000-0002-1603-9829)
- Yuwei Zhao (ORCID: https://orcid.org/0000-0003-2518-5016)
- Siwei Li (ORCID: https://orcid.org/0009-0005-6642-9859)
- Bing Liu (ORCID: https://orcid.org/0000-0003-3703-2842)
- Jing Zhou (ORCID: https://orcid.org/0000-0002-9167-0530)
- Qian Zhou (ORCID: https://orcid.org/0009-0000-0174-0762)
- Qi Zhang (ORCID: https://orcid.org/0009-0009-8549-5762)
Institutions
- Beijing Institute of Education (CN)
- Capital Medical University (CN)
- Chinese Academy of Sciences (CN)
- Peking University (CN)
- Harbin Institute of Technology (CN)
- Institute of Automation (CN)
- Beijing Computing Center (CN)
- Institute of Basic Medical Sciences of the Chinese Academy of Medical Sciences
Publication Details
- Journal
- Advanced Science
- Published
- 2025-06-30
- DOI
- https://doi.org/10.1002/advs.202503011
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Ministry of Science and Technology of the People's Republic of China