Kim Min-Ki, Sohn Jeong-Woo, Kim Sung-Phil
Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea.
Department of Medical Science, College of Medicine, Catholic Kwandong University, Gangneung, South Korea.
Front Neurosci. 2020 Oct 16;14:509364. doi: 10.3389/fnins.2020.509364. eCollection 2020.
The control of arm movements through intracortical brain-machine interfaces (BMIs) mainly relies on the activities of the primary motor cortex (M1) neurons and mathematical models that decode their activities. Recent research on decoding process attempts to not only improve the performance but also simultaneously understand neural and behavioral relationships. In this study, we propose an efficient decoding algorithm using a deep canonical correlation analysis (DCCA), which maximizes correlations between canonical variables with the non-linear approximation of mappings from neuronal to canonical variables via deep learning. We investigate the effectiveness of using DCCA for finding a relationship between M1 activities and kinematic information when non-human primates performed a reaching task with one arm. Then, we examine whether using neural activity representations from DCCA improves the decoding performance through linear and non-linear decoders: a linear Kalman filter (LKF) and a long short-term memory in recurrent neural networks (LSTM-RNN). We found that neural representations of M1 activities estimated by DCCA resulted in more accurate decoding of velocity than those estimated by linear canonical correlation analysis, principal component analysis, factor analysis, and linear dynamical system. Decoding with DCCA yielded better performance than decoding the original FRs using LSTM-RNN (6.6 and 16.0% improvement on average for each velocity and position, respectively; Wilcoxon rank sum test, < 0.05). Thus, DCCA can identify the kinematics-related canonical variables of M1 activities, thus improving the decoding performance. Our results may help advance the design of decoding models for intracortical BMIs.
通过皮层内脑机接口（BMI）控制手臂运动主要依赖于初级运动皮层（M1）神经元的活动以及对其活动进行解码的数学模型。最近关于解码过程的研究不仅试图提高性能，还同时理解神经与行为之间的关系。在本研究中，我们提出了一种使用深度典型相关分析（DCCA）的高效解码算法，该算法通过深度学习对从神经元变量到典型变量的映射进行非线性近似，从而最大化典型变量之间的相关性。我们研究了在非人类灵长类动物单臂执行伸手任务时，使用DCCA来寻找M1活动与运动学信息之间关系的有效性。然后，我们通过线性和非线性解码器：线性卡尔曼滤波器（LKF）和循环神经网络中的长短期记忆（LSTM - RNN），检验使用DCCA得到的神经活动表示是否能提高解码性能。我们发现，与通过线性典型相关分析、主成分分析、因子分析和线性动力系统估计的结果相比，由DCCA估计的M1活动的神经表示能更准确地解码速度。使用DCCA进行解码比使用LSTM - RNN解码原始发放率具有更好的性能（速度和位置的平均改善分别为6.6%和16.0%；Wilcoxon秩和检验，<0.05）。因此，DCCA可以识别与M1活动的运动学相关的典型变量，从而提高解码性能。我们的结果可能有助于推进皮层内BMI解码模型的设计。
