The investigation of the neural correlates of human gait, as measured by means of non-invasive electroencephalography (EEG), is of central importance for the understanding of human gait and for novel developments in gait rehabilitation. Particularly, gait-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 ERPs during spontaneous and instructed treadmill walking. A solution (hardware/software) 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 the temporal synchronization of recording devices, time-sensitive extraction of gait markers for the analysis of gERPs, and 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 investigated in a pilot study (n = 3 healthy participants, n = 3 females, mean age = 22.75 years). The recorded continuous EEG data were segmented into epochs according to the detected gait markers for the analysis of gERPs. Finally, the EEG epochs 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.

中文翻译：

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在指令性和自发性跑步机行走过程中测量与步态相关的脑电势（gERP）：技术解决方案和通过人工神经网络自动分类

通过非侵入性脑电图（EEG）来测量人的步态的神经相关性，对于理解人的步态和步态康复的新进展至关重要。特别是，与步态事件相关的脑电势（gERP）可以提供有关皮质大脑区域在人类步态控制中的功能作用的信息。本文的目的是探索在自发性和指示性跑步机行走过程中对步态ERP进行时间敏感分析的可能的实验和技术解决方案。开发，测试和试验了用于同步记录步态和EEG数据的解决方案（硬件/软件）。该解决方案包括一个定制的USB同步接口，一个时间同步模块和一个数据合并模块，允许记录设备的时间同步，对步态标记的时间敏感提取以分析gERP以及训练人工神经网络。在本手稿中，硬件和软件组件已通过以下设备进行了测试：带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系统（Brain Products GmbH）。在初步研究中研究了开发的解决方案的可用性和有效性（一种跑步机，带有用于步态分析的集成压力板（Zebris FDM-T）和Acticap非无线32通道EEG系统（Brain Products GmbH）。在初步研究中研究了开发的解决方案的可用性和有效性（一种跑步机，带有用于步态分析的集成压力板（Zebris FDM-T）和Acticap非无线32通道EEG系统（Brain Products GmbH）。在初步研究中研究了开发的解决方案的可用性和有效性（n = 3位健康参与者，n = 3位女性，平均年龄= 22.75岁。根据检测到的步态标记，将记录的连续EEG数据分为几个时期，以分析gERP。最后，EEG时代被用来训练深度学习人工神经网络，作为步态阶段的分类器。这项初步研究获得的结果尽管是初步的，但仍支持该解决方案在步态相关脑电图分析中的可行性。