Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges
Understanding how the brain encodes upper limb movements is crucial for developing control mechanisms in assistive technologies. Advances in assistive technologies, particularly Brain-machine Interfaces (BMIs), highlight the importance of decoding motor intentions and kinematics for effective contro...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1532783/full |
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| author | Sutirtha Ghosh Rohit Kumar Yadav Sunaina Soni Shivangi Giri Shivangi Giri Suriya Prakash Muthukrishnan Lalan Kumar Lalan Kumar Shubhendu Bhasin Sitikantha Roy |
| author_facet | Sutirtha Ghosh Rohit Kumar Yadav Sunaina Soni Shivangi Giri Shivangi Giri Suriya Prakash Muthukrishnan Lalan Kumar Lalan Kumar Shubhendu Bhasin Sitikantha Roy |
| author_sort | Sutirtha Ghosh |
| collection | DOAJ |
| description | Understanding how the brain encodes upper limb movements is crucial for developing control mechanisms in assistive technologies. Advances in assistive technologies, particularly Brain-machine Interfaces (BMIs), highlight the importance of decoding motor intentions and kinematics for effective control. EEG-based BMI systems show promise due to their non-invasive nature and potential for inducing neural plasticity, enhancing motor rehabilitation outcomes. While EEG-based BMIs show potential for decoding motor intention and kinematics, studies indicate inconsistent correlations with actual or planned movements, posing challenges for achieving precise and reliable prosthesis control. Further, the variability in predictive EEG patterns across individuals necessitates personalized tuning to improve BMI efficiency. Integrating multiple physiological signals could enhance BMI precision and reliability, paving the way for more effective motor rehabilitation strategies. Studies have shown that brain activity adapts to gravitational and inertial constraints during movement, highlighting the critical role of neural adaptation to biomechanical changes in creating control systems for assistive devices. This review aims to provide a comprehensive overview of recent progress in deciphering neural activity patterns associated with both physiological and assisted upper limb movements, highlighting avenues for future exploration in neurorehabilitation and brain-machine interface development. |
| format | Article |
| id | doaj-art-ec4636cc101144cfac1669e8d1700a39 |
| institution | Kabale University |
| issn | 1662-5161 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj-art-ec4636cc101144cfac1669e8d1700a392025-02-06T07:10:19ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612025-02-011910.3389/fnhum.2025.15327831532783Decoding the brain-machine interaction for upper limb assistive technologies: advances and challengesSutirtha Ghosh0Rohit Kumar Yadav1Sunaina Soni2Shivangi Giri3Shivangi Giri4Suriya Prakash Muthukrishnan5Lalan Kumar6Lalan Kumar7Shubhendu Bhasin8Sitikantha Roy9Department of Physiology, All India Institute of Medical Sciences, New Delhi, IndiaDepartment of Physiology, All India Institute of Medical Sciences, New Delhi, IndiaDepartment of Physiology, All India Institute of Medical Sciences, New Delhi, IndiaDepartment of Biomedical Engineering, National Institute of Technology, Raipur, IndiaDepartment of Applied Mechanics, Indian Institute of Technology Delhi, New Delhi, IndiaDepartment of Physiology, All India Institute of Medical Sciences, New Delhi, IndiaDepartment of Electrical Engineering, Bharti School of Telecommunication, New Delhi, IndiaYardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, IndiaDepartment of Applied Mechanics, Indian Institute of Technology Delhi, New Delhi, IndiaUnderstanding how the brain encodes upper limb movements is crucial for developing control mechanisms in assistive technologies. Advances in assistive technologies, particularly Brain-machine Interfaces (BMIs), highlight the importance of decoding motor intentions and kinematics for effective control. EEG-based BMI systems show promise due to their non-invasive nature and potential for inducing neural plasticity, enhancing motor rehabilitation outcomes. While EEG-based BMIs show potential for decoding motor intention and kinematics, studies indicate inconsistent correlations with actual or planned movements, posing challenges for achieving precise and reliable prosthesis control. Further, the variability in predictive EEG patterns across individuals necessitates personalized tuning to improve BMI efficiency. Integrating multiple physiological signals could enhance BMI precision and reliability, paving the way for more effective motor rehabilitation strategies. Studies have shown that brain activity adapts to gravitational and inertial constraints during movement, highlighting the critical role of neural adaptation to biomechanical changes in creating control systems for assistive devices. This review aims to provide a comprehensive overview of recent progress in deciphering neural activity patterns associated with both physiological and assisted upper limb movements, highlighting avenues for future exploration in neurorehabilitation and brain-machine interface development.https://www.frontiersin.org/articles/10.3389/fnhum.2025.1532783/fullEEGvoluntary movementmovement related cortical potentialevent-related desynchronization/synchronizationhuman-machine interaction |
| spellingShingle | Sutirtha Ghosh Rohit Kumar Yadav Sunaina Soni Shivangi Giri Shivangi Giri Suriya Prakash Muthukrishnan Lalan Kumar Lalan Kumar Shubhendu Bhasin Sitikantha Roy Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges Frontiers in Human Neuroscience EEG voluntary movement movement related cortical potential event-related desynchronization/synchronization human-machine interaction |
| title | Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges |
| title_full | Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges |
| title_fullStr | Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges |
| title_full_unstemmed | Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges |
| title_short | Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges |
| title_sort | decoding the brain machine interaction for upper limb assistive technologies advances and challenges |
| topic | EEG voluntary movement movement related cortical potential event-related desynchronization/synchronization human-machine interaction |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2025.1532783/full |
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