Background: For the nonstationarity of neural recordings in intracortical brain–machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinforcement learning (RL) based self-recalibrating decoder. However, quickly exploring new knowledge while maintaining a good performance remains a challenge in RL-based decoders. Methods: To solve this problem, we proposed an attention-gated RL-based algorithm combining transfer learning, mini-batch, and weight updating schemes to accelerate the weight updating and avoid over-fitting. The proposed algorithm was tested on intracortical neural data recorded from two monkeys to decode their reaching positions and grasping gestures. Results: The decoding results showed that our proposed algorithm achieved an approximate 20% increase in classification accuracy compared to that obtained by the non-retrained classifier and even achieved better classification accuracy than the daily retraining classifier. Moreover, compared with a conventional RL method, our algorithm improved the accuracy by approximately 10% and the online weight updating speed by approximately 70 times. Conclusions: This paper proposed a self-recalibrating decoder which achieved a good and robust decoding performance with fast weight updating and might facilitate its application in wearable device and clinical practice.

中文翻译：

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基于增强学习的皮层内脑机接口快速自校准解码器

背景：由于皮质内脑机接口中神经记录的不稳定，始终需要以监督的方式进行每日重新训练，以保持解码器的性能。通过使用基于增强学习（RL）的自校准解码器，可以改善此问题。但是，在基于RL的解码器中，在保持良好性能的同时快速探索新知识仍然是一个挑战。方法：为解决此问题，我们提出了一种基于注意门控的基于RL的算法，该算法结合了转移学习，小批量和权重更新方案，以加快权重更新并避免过度拟合。该算法对两只猴子记录的大脑皮层内神经数据进行了测试，以解码它们的到达位置并掌握手势。结果：解码结果表明，与非训练分类器相比，我们提出的算法的分类精度提高了约20％，甚至比日常训练分类器获得了更好的分类精度。此外，与传统的RL方法相比，我们的算法将精度提高了约10％，在线权重更新速度提高了约70倍。结论：本文提出了一种自校准解码器，该解码器在快速更新权重的情况下获得了良好而强大的解码性能，并可能有助于其在可穿戴设备和临床实践中的应用。此外，与传统的RL方法相比，我们的算法将精度提高了约10％，在线权重更新速度提高了约70倍。结论：本文提出了一种自校准解码器，该解码器在快速更新权重的情况下获得了良好而强大的解码性能，并可能有助于其在可穿戴设备和临床实践中的应用。此外，与传统的RL方法相比，我们的算法将精度提高了约10％，在线权重更新速度提高了约70倍。结论：本文提出了一种自校准解码器，该解码器在快速更新权重的情况下获得了良好而强大的解码性能，并可能有助于其在可穿戴设备和临床实践中的应用。