A Data Driven Experimental System for Individualized Brain Stimulation Design and Validation
A Data Driven Experimental System for Individualized Brain Stimulation Design and Validation
复制标题
用于个性化大脑刺激设计和验证的数据驱动实验系统
DOI：
10.1109/tnsre.2021.3110275
复制
发表时间：
2021-01-01
影响因子：
4.9
通讯作者：
Wei, Xile
中科院分区：
文献类型：
--
作者：
Chang, Siyuan;Wang, Jiang;Wei, Xile
脑深部电刺激（DBS）是治疗癫痫的有效方法。然而，DBS参数的个性化设置和自适应调整仍然面临着巨大的挑战。本文研究了一种数据驱动的硬件在环（HIL）实验系统，用于闭环脑刺激系统的个性化设计和验证。利用无迹卡尔曼滤波（UKF）从脑电记录中估计神经质量模型（NMM）的关键参数，重建个体神经活动。基于重构的神经网络模型，我们构建了一个基于数字信号处理器（DSP）的具有真实的时间尺度和生物信号电平尺度的虚拟脑平台。然后，设计了相应的信号放大检测和闭环控制器等硬件部分，构成了半实物仿真实验系统。基于所设计的实验系统，针对不同的个体NMM设计了比例积分控制器，并进行了实验验证，证明了实验系统的有效性。该实验系统提供了一个平台，以探索脑刺激下的神经活动和各种闭环刺激范式的影响。
Deep brain stimulation (DBS) is an effective clinical treatment for epilepsy. However, the individualized setting and adaptive adjustment of DBS parameters are still facing great challenges. This paper investigates a data-driven hardware-in-the-loop (HIL) experimental system for closed-loop brain stimulation system individualized design and validation. The unscented Kalman filter (UKF) is utilized to estimate critical parameters of neural mass model (NMM) from the electroencephalogram recordings to reconstruct individual neural activity. Based on the reconstructed NMM, we build a digital signal processor (DSP) based virtual brain platform with real time scale and biological signal level scale. Then, the corresponding hardware parts of signal amplification detection and closed-loop controller are designed to form the HIL experimental system. Based on the designed experimental system, the proportional-integral controller for different individual NMM is designed and validated, which proves the effectiveness of the experimental system. This experimental system provides a platform to explore neural activity under brain stimulation and the effects of various closed-loop stimulation paradigms.