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 …

Advances in machine learning technologies in recent years have facilitated developments in
autonomous robotic systems. Designing these autonomous systems typically requires …

Significance: A number of movement intent decoders exist in the literature that typically differ
in the algorithms used and the nature of the outputs generated. Each approach comes with …

This paper presents a framework for modeling neural decoding using electromyogram
(EMG) and electrocorticogram (ECoG) signals to interpret human intent and control …

Brain-machine interfaces (BMIs) enable people with disabilities to control external devices
with their motor intentions through a decoder. Compared with supervised learning …

Kernel temporal differences (KTD) (λ) algorithm integrated in Q-learning (Q-KTD) has shown
its applicability and feasibility for reinforcement learning brain machine interfaces (RLBMIs) …

Advances in machine learning technologies in recent years have facilitated developments in
autonomous robotic systems. Designing these autonomous systems typically requires …

The problem of Markov decision processes (MDPs) with uncertain observation noise has
rarely been studied. This paper proposes a Robust Quadratic Programming (RQP) approach …