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Measuring Gait-Event-Related Brain Potentials (gERPs) during Instructed and Spontaneous Treadmill Walking: Technical Solutions and Automated Classification through Artificial Neural Networks

1
Department of Applied Emotion and Motivation Psychology, Institute of Psychology and Education, Ulm University, 89081 Ulm, Germany
2
Department of Medical Engineering and Mechatronics, Ulm University of Applied Sciences, 89075 Ulm, Germany
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(16), 5405; https://doi.org/10.3390/app10165405
Received: 18 June 2020 / Revised: 26 July 2020 / Accepted: 27 July 2020 / Published: 5 August 2020
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. View Full-Text
Keywords: human gait analysis; machine learning; motor potentials; event-related potentials (ERPs); gait-ERPs; cognition human gait analysis; machine learning; motor potentials; event-related potentials (ERPs); gait-ERPs; cognition
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MDPI and ACS Style

Herbert, C.; Munz, M. Measuring Gait-Event-Related Brain Potentials (gERPs) during Instructed and Spontaneous Treadmill Walking: Technical Solutions and Automated Classification through Artificial Neural Networks. Appl. Sci. 2020, 10, 5405. https://doi.org/10.3390/app10165405

AMA Style

Herbert C, Munz M. Measuring Gait-Event-Related Brain Potentials (gERPs) during Instructed and Spontaneous Treadmill Walking: Technical Solutions and Automated Classification through Artificial Neural Networks. Applied Sciences. 2020; 10(16):5405. https://doi.org/10.3390/app10165405

Chicago/Turabian Style

Herbert, Cornelia, and Michael Munz. 2020. "Measuring Gait-Event-Related Brain Potentials (gERPs) during Instructed and Spontaneous Treadmill Walking: Technical Solutions and Automated Classification through Artificial Neural Networks" Applied Sciences 10, no. 16: 5405. https://doi.org/10.3390/app10165405

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