Reinforcement learning (RL)-based brain machine interfaces (BMIs) assist paralyzed people in controlling neural prostheses without the need for real limb movement as supervised signals. The design of reward signal significantly impacts the learning efficiency of the RL-based decoders. Existing reward designs in the RL-based BMI framework rely on external rewards or manually labeled in... https://www.markbroyard.com/