论文标题
在指导和自发跑步机步行期间步态事件相关的大脑电位分析 - 技术负担和使用的方法
Analysis of Gait-Event-related Brain Potentials During Instructed And Spontaneous Treadmill Walking -- Technical Affordances and used Methods
论文作者
论文摘要
为了提高对人体步态的理解并促进步态康复的新发展，最近研究了通过非侵入性脑电图（EEG）衡量的人步态的神经相关性。特别是，与步态相关事件相关的大脑电位（GERP）可能会提供有关皮质大脑区域在人步态控制中的功能作用的信息。本文的目的是探索可能在自发和指示跑步机步行期间对与人步态相关的ERP的时间敏感分析的可能实验和技术解决方案。开发，测试和试验，用于步态和脑电图数据同步记录的解决方案（HW/SW）。该解决方案由定制的USB同步界面，时间同步模块和数据合并模块组成，从而使记录设备的时间同步用于时间敏感的步态标记，以分析步态相关的ERP和人工神经网络的训练。在本手稿中，使用以下设备对硬件和软件组件进行了测试：具有用于步态分析的集成压力板的跑步机（Zebris FDM-T）和Acticap非无线32通道EEG-EEG-SYSTEM（脑产品GMBH）。在一项试点研究中测试了开发解决方案的可用性和有效性（n = 3个健康参与者，n = 3女性，平均年龄= 22.75岁）。根据检测到的步态标记对步态相关ERP的分析进行了分割和分析记录的脑电图数据。最后，EEG时期被用来训练一个深度学习的人工神经网络作为步态阶段的分类器。在这项试验研究中获得的结果尽管初步，但支持解决方案应用与步态相关的脑电图分析的可行性。
To improve the understanding of human gait and to facilitate novel developments in gait rehabilitation, the neural correlates of human gait as measured by means of non-invasive electroencephalography (EEG) have been investigated recently. Particularly, gait-related event-related brain potentials (gERPs) may provide information about the functional role of cortical brain regions in human gait control. The purpose of this paper is to explore possible experimental and technical solutions for time-sensitive analysis of human gait-related ERPs during spontaneous and instructed treadmill walking. A solution (HW/SW) for synchronous recording of gait- and EEG data was developed, tested and piloted. The solution consists of a custom-made USB synchronization interface, a time-synchronization module and a data merging module, allowing temporal synchronization of recording devices for time-sensitive extraction of gait markers for analysis of gait-related ERPs and for the training of artificial neural networks. In the present manuscript, the hardware and software components were tested with the following devices: A treadmill with an integrated pressure plate for gait analysis (zebris FDM-T) and an Acticap non-wireless 32-channel EEG-system (Brain Products GmbH). The usability and validity of the developed solution was tested in a pilot study (n = 3 healthy participants, n=3 females, mean age = 22.75 years). Recorded EEG data was segmented and analyzed according to the detected gait markers for the analysis of gait-related ERPs. Finally, EEG periods were used to train a deep learning artificial neural network as classifier of gait phases. The results obtained in this pilot study, although preliminary, support the feasibility of the solution for the application of gait-related EEG analysis..
