Hidden Brain State-Based Internal Evaluation Using Kernel Inverse Reinforcement Learning in Brain-Machine Interfaces
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 rewar...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10759843/ |