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The design of brain machine interfaces (BMI) has been improving over the past decade. Such improvements have led to advanced capability in terms of restoring the functionality of a paralyzed/amputated limb and producing fine controlled movements of a robotic arm and hand. However, there is still more to be invested towards producing advanced BMI features such as producing appropriate forces when gripping...
Increasingly accurate control of prosthetic limbs has been made possible by a series of advancements in brain machine interface (BMI) control theory. One promising control technique for future BMI applications is reinforcement learning (RL). RL based BMIs require a reinforcing signal to inform the controller whether or not a given movement was intended by the user. This signal has been shown to exist...
Improving the control of neuroprosthetics to achieve biomimetic movements would significantly increase their utility and greatly improve the quality of life of their users. One potential addition to today's neuroprosthetics control systems would be an inclusion of the reward-based signal from motor or somatosensory cortex. The reward signal present in these cortices could indicate if a movement goal,...
Movement decoding algorithms used in today's brain-machine interface (BMI) technologies require movement-related neural activity in large quantities as training data to decode with sufficient accuracy the intended movements of the user. Because of physical disability the end users of BMI systems may be unable to readily provide such training data. Moreover, variability in the neural control of movements...
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