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Clustering Based Kernel Reinforcement Learning for Neural Adaptation in Brain-Machine Interfaces.

Xiang Zhang, Jose C Principe, Yiwen Wang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    This study introduces a novel clustering-based reinforcement learning algorithm for Brain Machine Interfaces (BMIs). The new method improves learning speed and accuracy by efficiently exploring neural data, outperforming existing techniques.

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    Area of Science:

    • Neuroscience
    • Machine Learning
    • Biomedical Engineering

    Background:

    • Reinforcement learning (RL) decodes movement intention in Brain Machine Interfaces (BMIs) without requiring actual limb movements.
    • Adapting to brain control expands the state-action space, posing challenges for efficient exploration and performance maintenance in RL decoders.
    • Existing Quantized Attention-Gated Kernel Reinforcement Learning (QAGKRL) faces computational inefficiency and sensitivity issues with growing network size and input structures.

    Purpose of the Study:

    • To develop a more computationally efficient and accurate kernel-based reinforcement learning algorithm for BMIs.
    • To address the limitations of existing methods in handling large state-action spaces and new input data during brain control adaptation.

    Main Methods:

    • Proposed a novel kernel-based reinforcement learning algorithm utilizing input domain clustering.
    • Similar neural inputs are grouped into clusters, with new inputs activating nearest clusters to utilize or form sub-networks.
    • This approach creates sub-feature spaces for output calculation, enhancing knowledge transfer and reducing computational complexity compared to global mapping.

    Main Results:

    • The proposed algorithm demonstrated a faster learning curve on synthetic spike data simulating manual and brain control task mode switches.
    • Achieved reduced computational time and increased accuracy compared to the QAGKRL method.
    • Simulation results indicate superior performance in adapting to changing control conditions.

    Conclusions:

    • The clustering-based kernel reinforcement learning algorithm offers significant improvements in learning efficiency and computational performance for BMIs.
    • The method effectively transfers knowledge and reduces computational load by utilizing sub-feature spaces.
    • This algorithm presents a promising approach for real-time (online) implementation in Brain Machine Interface systems.