Measuring gait-event-related brain potentials (gERPs) during instructed and spontaneous treadmill walking: technical solutions and automated classification through artificial neural networks
peer-reviewed
Erstveröffentlichung
2020-08-05Authors
Herbert, Cornelia
Munz, Michael
Wissenschaftlicher Artikel
Published in
Applied Sciences ; 10 (2020), 16. - Art.-Nr. 5405. - eISSN 2076-3417
Link to original publication
https://dx.doi.org/10.3390/app10165405Faculties
Fakultät für Ingenieurwissenschaften, Informatik und PsychologieInstitutions
Institut für Psychologie und PädagogikExternal cooperations
Technische Hochschule UlmeISSN
2076-3417
Document version
published version (publisher's PDF)Abstract
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.
Publication funding
Gefördert vom Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg
Open-Access-Förderung durch die Universität Ulm
Open-Access-Förderung durch die Universität Ulm
Subject headings
[GND]: Neuronales Netz | Maschinelles Lernen | Elektroencephalographie[LCSH]: Neural networks (Computer science) | Machine learning | Locomotion | Cognition | Attention
[MeSH]: Gait | Electroencephalography
[Free subject headings]: Human gait analysis | Motor potentials | Event-related potentials (ERPs) | Gait-ERPs | Desynchronization
[DDC subject group]: DDC 530 / Physics | DDC 540 / Chemistry & allied sciences | DDC 620 / Engineering & allied operations
Metadata
Show full item recordDOI & citation
Please use this identifier to cite or link to this item: http://dx.doi.org/10.18725/OPARU-34032
Herbert, Cornelia; Munz, Michael (2020): Measuring gait-event-related brain potentials (gERPs) during instructed and spontaneous treadmill walking: technical solutions and automated classification through artificial neural networks. Open Access Repositorium der Universität Ulm und Technischen Hochschule Ulm. http://dx.doi.org/10.18725/OPARU-34032
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