Zhang Mingming, Schwemmer Michael A, Ting Jordyn E, Majstorovic Connor E, Friedenberg David A, Bockbrader Marcia A, Jerry Mysiw W, Rezai Ali R, Annetta Nicholas V, Bouton Chad E, Bresler Herbert S, Sharma Gaurav
1Battelle Memorial Institute, 505 King Ave, Columbus, OH 43021 USA.
2Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH 43210 USA.
Bioelectron Med. 2018 Jul 31;4:11. doi: 10.1186/s42234-018-0011-x. eCollection 2018.
Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain.
In this study, we show the applicability of wavelet decomposition method to extract and demonstrate the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP (-MWP, 0-234 Hz), mid-frequency MWP (-MWP, 234 Hz-3.75 kHz) and high-frequency MWP (-MWP, >3.75 kHz). We analyzed these features using data collected from two experiments that were repeated over the course of about 3 years and compared their signal stability and decoding performance with the more standard threshold crossings, local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings.
All neural features could stably track neural information for over 3 years post-implantation and were less prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector machine based decoding algorithms, the -MWP and MUA demonstrated significantly better performance, respectively, in classifying imagined motor tasks than using the -MWP, -MWP, LFP, or threshold crossings.
Our results suggest that using MWP features in the appropriate frequency bands can provide an effective neural feature for brain computer interface intended for chronic applications.
This study was approved by the U.S. Food and Drug Administration (Investigational Device Exemption) and the Ohio State University Medical Center Institutional Review Board (Columbus, Ohio). The study conformed to institutional requirements for the conduct of human subjects and was filed on ClinicalTrials.gov (Identifier NCT01997125).
了解皮层内记录信号的长期行为对于提高脑机接口的性能至关重要。然而，很少有研究系统地调查来自植入人类大脑的微电极阵列的慢性神经记录。
在本研究中，我们展示了小波分解方法在提取人类四肢瘫痪患者运动皮层植入微电极阵列获得的神经信号中的长期稳定特征并证明其效用方面的适用性。将小波分解应用于原始电压数据以生成平均小波功率（MWP）特征，这些特征进一步分为三个子频段，低频MWP（-MWP，0 - 234 Hz）、中频MWP（-MWP，234 Hz - 3.75 kHz）和高频MWP（-MWP，>3.75 kHz）。我们使用从大约3年的过程中重复进行的两个实验收集的数据来分析这些特征，并将它们的信号稳定性和解码性能与从原始电压记录中获得的更标准的阈值穿越、局部场电位（LFP）、多单元活动（MUA）特征进行比较。
所有神经特征在植入后3年以上都能稳定地跟踪神经信息，并且与阈值穿越相比，更不容易出现信号退化。此外，当用作基于支持向量机的解码算法的输入时，-MWP和MUA在对想象的运动任务进行分类时分别表现出比使用-MWP、-MWP、LFP或阈值穿越明显更好的性能。
我们的结果表明，在适当的频段使用MWP特征可以为用于慢性应用的脑机接口提供有效的神经特征。
本研究获得了美国食品药品监督管理局（研究性器械豁免）和俄亥俄州立大学医学中心机构审查委员会（俄亥俄州哥伦布）的批准。该研究符合机构对人类受试者研究的要求，并已在ClinicalTrials.gov上备案（标识符NCT01997125）。
