ArticleComputational intelligence and neuroscience2018
Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate.
Article in Computational intelligence and neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Application of a neural interface for restoration of leg movements: Intra-spinal stimulation using the brain electrical activity in spinally injured rabbits.Journal of applied biomedicine · 2020Article
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4 authors.
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Abstract
Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology.
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