期刊
SCIENTIFIC REPORTS
卷 6, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/srep21781
关键词
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资金
- Ministry of Education, Culture, Sports, Science and Technology (MEXT) [24700419, 2656046, 15H05710]
- Precursory Research for Embryonic Science and Technology (PRESTO) program from the Japan Science and Technology Agency (JST)
- Strategic Research Program for Brain Sciences (SRPBS) by the MEXT
- Japan Agency for Medical Research and Development (AMED)
- AMED
- ImPACT Program of the Council for Science, Technology and Innovation (Cabinet Office, Government of Japan)
- Casio Science Promotion Foundation
- Japan Foundation for Aging and Health
- Grants-in-Aid for Scientific Research [15H05710, 26560467, 26282165, 24700419] Funding Source: KAKEN
Neuroprosthetic arms might potentially restore motor functions for severely paralysed patients. Invasive measurements of cortical currents using electrocorticography have been widely used for neuroprosthetic control. Moreover, magnetoencephalography (MEG) exhibits characteristic brain signals similar to those of invasively measured signals. However, it remains unclear whether noninvasively measured signals convey enough motor information to control a neuroprosthetic hand, especially for severely paralysed patients whose sensorimotor cortex might be reorganized. We tested an MEG-based neuroprosthetic system to evaluate the accuracy of using cortical currents in the sensorimotor cortex of severely paralysed patients to control a prosthetic hand. The patients attempted to grasp with or open their paralysed hand while the slow components of MEG signals (slow movement fields; SMFs) were recorded. Even without actual movements, the SMFs of all patients indicated characteristic spatiotemporal patterns similar to actual movements, and the SMFs were successfully used to control a neuroprosthetic hand in a closed-loop condition. These results demonstrate that the slow components of MEG signals carry sufficient information to classify movement types. Successful control by paralysed patients suggests the feasibility of using an MEG-based neuroprosthetic hand to predict a patient's ability to control an invasive neuroprosthesis via the same signal sources as the noninvasive method.
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