Abstract
To determine whether simultaneously recorded motor cortex neurons can be used for real-time device control, rats were trained to position a robot arm to obtain water by pressing a lever. Mathematical transformations, including neural networks, converted multineuron signals into 'neuronal population functions' that accurately predicted lever trajectory. Next, these functions were electronically converted into real-time signals for robot arm control. After switching to this 'neurorobotic' mode, 4 of 6 animals (those with >25 task-related neurons) routinely used these brain-derived signals to position the robot arm and obtain water. With continued training in neurorobotic mode, the animals' lever movement diminished or stopped. These results suggest a possible means for movement restoration in paralysis patients.
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Somatosensory ECoG-based brain–machine interface with electrical stimulation on medial forebrain bundle
Biomedical Engineering Letters Open Access 20 December 2022
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Acknowledgements
This work was supported by NIH contract NS62352, ONR grant N00014-98-1-0679 and NIH grant NS26722 to J.K.C.
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Chapin, J., Moxon, K., Markowitz, R. et al. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci 2, 664–670 (1999). https://doi.org/10.1038/10223
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DOI: https://doi.org/10.1038/10223
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