State Key Laboratory of Mechanics and Control of Mechanical Structures, Institute of Nano Science, and Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, People's Republic of China.
J Neural Eng. 2022 Sep 2;19(4). doi: 10.1088/1741-2552/ac88a0.
Understanding neural encoding and decoding processes are crucial to the development of brain-machine interfaces (BMI). Higher decoding speed of neural signals is required for the large-scale neural data and the extremely low detection delay of closed-loop feedback experiment.To achieve higher neural decoding speed, we proposed a novel adaptive higher-order nonlinear point-process filter based on the variational Bayesian inference (VBI) framework, called the HON-VBI. This algorithm avoids the complex Monte Carlo random sampling in the traditional method. Using the VBI method, it can quickly implement inferences of state posterior distribution and the tuning parameters.Our result demonstrates the effectiveness and advantages of the HON-VBI by application for decoding the multichannel neural spike trains of the simulation data and real data. Compared with traditional methods, the HON-VBI greatly reduces the decoding time of large-scale neural spike trains. Through capturing the nonlinear evolution of system state and accurate estimating of time-varying tuning parameters, the decoding accuracy is improved.Our work can be applied to rapidly decode large-scale multichannel neural spike trains in BMIs.
理解神经编码和解码过程对于脑机接口（BMI）的发展至关重要。为了实现大规模神经数据的高速解码和闭环反馈实验的极低检测延迟，需要提高神经信号的解码速度。为了实现更高的神经解码速度，我们提出了一种新的基于变分贝叶斯推断（VBI）框架的自适应高阶非线性点过程滤波器，称为 HON-VBI。该算法避免了传统方法中复杂的蒙特卡罗随机采样。使用 VBI 方法，可以快速实现状态后验分布和调谐参数的推断。我们的结果通过应用于模拟数据和真实数据的多通道神经尖峰列车的解码，证明了 HON-VBI 的有效性和优势。与传统方法相比，HON-VBI 大大减少了大规模神经尖峰列车的解码时间。通过捕捉系统状态的非线性演变和准确估计时变调谐参数，提高了解码精度。我们的工作可以应用于快速解码 BMI 中的大规模多通道神经尖峰列车。
