Assistive technologies (AT) enable people with disabilities to perform activities of daily living
more independently, have greater access to community and healthcare services, and be …

Significance: The performance of traditional approaches to decoding movement intent from
electromyograms (EMGs) and other biological signals commonly degrade over time …

Reinforcement-learning (RL)-based brain-machine interfaces (BMIs) interpret dynamic
neural activity into movement intention without patients' real limb movements, which is …

Creating flexible and robust brain machine interfaces (BMIs) is currently a popular topic of
research that has been explored for decades in medicine, engineering, commercial, and …

Reinforcement learning (RL)-based decoders in brain-machine interfaces (BMIs) interpret
dynamic neural activity without patients' real limb movements. In conventional RL, the goal …

Neuroprosthesis enables the brain control on the external devices purely using neural
activity for paralyzed people. Supervised learning decoders recalibrate or re-fit the …

Autonomous brain machine interfaces (BMIs) aim to enable paralyzed people to self-
evaluate their movement intention to control external devices. Previous reinforcement …

Reinforcement learning (RL) is an artificial intelligence algorithm that can learn adaptive
optimal control law online. In view of the fact that the previous control approaches were …

Goal: To identify and overcome barriers to creating new neurotechnologies capable of
restoring both motor and sensory function in individuals with neurological conditions …

Modeling spike train transformation among brain regions helps in designing a cognitive
neural prosthesis that restores lost cognitive functions. Various methods analyze the …