Estimation of Gait Parameters from EEG Source Oscillations

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Long-term impairment, disability and handicap are major issues after stroke. A wide range of interventions have been developed that aim to promote motor recovery in affected persons. High-intensity and task-specific training protocols show promising results. A better understanding of brain functioning in the context of motor learning and motor control may help to further improve rehabilitation outcome. Mobile brain imaging has brought advances that led to the development of models that characterize different aspects of the cortical involvement in movement. We are interested in translating those findings into online applications and lay a basis for novel rehabilitation interventions. In this paper, we use a model of gait consisting of two parameters: The state of walking (compared to upright standing) and the dynamics of the movement, i.e. the gait cadence. To this end, we perform mobile electroencephalography (EEG) measurements combined with inverse brain imaging and time-frequency analyses optimized for online application.
Original languageEnglish
Title of host publicationSystems, Man, and Cybernetics (SMC)
PublisherIEEE Computer Society
Pages004182 - 004187
ISBN (Electronic)978-1-5090-1897-0
Publication statusPublished - 2017
Event2016 IEEE International Conference on Systems, Man, and Cybernetics - Budapest, Hungary
Duration: 9 Oct 201612 Oct 2016


Conference2016 IEEE International Conference on Systems, Man, and Cybernetics

Fields of Expertise

  • Human- & Biotechnology

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)


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