Lin Yu-Chieh, Chou Chin, Yang Shin-Hung, Lai Hsin-Yi, Lo Yu-Chun, Chen You-Yin
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2539-2542. doi: 10.1109/EMBC.2018.8512775.
Changes in the functional mapping between neural activities and kinematic parameters over time poses a challenge to current neural decoder of brain machine interfaces (BMIs). Traditional decoders robust to changes in functional mappings required many day's training data. The decoder may not be robust when it was trained by data from only few days. Therefore, a decoder should be trained to handle a variety of neural-to-kinematic mappings using limited training data. We proposed an evolutionary neural network with error feedback, ECPNN-EF, as a neural decoder, that considered the previous error as an input to the decoder in order to improve the robustness. The decoder was validated to reconstruct rat's forelimb movement in a water-reward lever-pressing task. Two days of data were only used to train the decoder while ten days of data were used to test the decoder. The results showed that the performance of ECPNN-EF was significantly higher than that of standard recurrent neural network without error feedback, which was commonly used in BMI. This suggested that ECPNN-EF trained with few days of training data can be robust to changes in functional mappings.
神经活动与运动学参数之间的功能映射随时间的变化给当前脑机接口（BMI）的神经解码器带来了挑战。对功能映射变化具有鲁棒性的传统解码器需要许多天的训练数据。当仅使用几天的数据进行训练时，该解码器可能不具有鲁棒性。因此，应该训练一个解码器，以便使用有限的训练数据来处理各种神经到运动学的映射。我们提出了一种带有误差反馈的进化神经网络ECPNN-EF作为神经解码器，它将先前的误差作为解码器的输入，以提高鲁棒性。该解码器在水奖励杠杆按压任务中被验证可重建大鼠的前肢运动。仅使用两天的数据来训练解码器，而使用十天的数据来测试解码器。结果表明，ECPNN-EF的性能显著高于BMI中常用的无误差反馈的标准循环神经网络。这表明，用几天的训练数据训练的ECPNN-EF对功能映射的变化具有鲁棒性。
