Estimation of Gait Parameters from EEG Source Oscillations

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

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.
Originalspracheenglisch
TitelSystems, Man, and Cybernetics (SMC)
Herausgeber (Verlag)IEEE Computer Society
Seiten004182 - 004187
ISBN (elektronisch)978-1-5090-1897-0
DOIs
PublikationsstatusVeröffentlicht - 2017
Veranstaltung2016 IEEE International Conference on Systems, Man, and Cybernetics - Budapest, Ungarn
Dauer: 9 Okt 201612 Okt 2016

Konferenz

Konferenz2016 IEEE International Conference on Systems, Man, and Cybernetics
LandUngarn
OrtBudapest
Zeitraum9/10/1612/10/16

Fingerprint

Gait
Neuroimaging
Electroencephalography
Walking
Rehabilitation
Stroke
Learning
Brain

Fields of Expertise

  • Human- & Biotechnology

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)

Dies zitieren

Hehenberger, L., Seeber, M., & Scherer, R. (2017). Estimation of Gait Parameters from EEG Source Oscillations. in Systems, Man, and Cybernetics (SMC) (S. 004182 - 004187). IEEE Computer Society. https://doi.org/10.1109/SMC.2016.7844888

Estimation of Gait Parameters from EEG Source Oscillations. / Hehenberger, Lea; Seeber, Martin; Scherer, Reinhold.

Systems, Man, and Cybernetics (SMC). IEEE Computer Society, 2017. S. 004182 - 004187.

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Hehenberger, L, Seeber, M & Scherer, R 2017, Estimation of Gait Parameters from EEG Source Oscillations. in Systems, Man, and Cybernetics (SMC). IEEE Computer Society, S. 004182 - 004187, Budapest, Ungarn, 9/10/16. https://doi.org/10.1109/SMC.2016.7844888
Hehenberger L, Seeber M, Scherer R. Estimation of Gait Parameters from EEG Source Oscillations. in Systems, Man, and Cybernetics (SMC). IEEE Computer Society. 2017. S. 004182 - 004187 https://doi.org/10.1109/SMC.2016.7844888
Hehenberger, Lea ; Seeber, Martin ; Scherer, Reinhold. / Estimation of Gait Parameters from EEG Source Oscillations. Systems, Man, and Cybernetics (SMC). IEEE Computer Society, 2017. S. 004182 - 004187
@inproceedings{76cc0cc4a15542908e13e630d483b298,
title = "Estimation of Gait Parameters from EEG Source Oscillations",
abstract = "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.",
author = "Lea Hehenberger and Martin Seeber and Reinhold Scherer",
year = "2017",
doi = "10.1109/SMC.2016.7844888",
language = "English",
pages = "004182 -- 004187",
booktitle = "Systems, Man, and Cybernetics (SMC)",
publisher = "IEEE Computer Society",
address = "United States",

}

TY - GEN

T1 - Estimation of Gait Parameters from EEG Source Oscillations

AU - Hehenberger, Lea

AU - Seeber, Martin

AU - Scherer, Reinhold

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

U2 - 10.1109/SMC.2016.7844888

DO - 10.1109/SMC.2016.7844888

M3 - Conference contribution

SP - 4182

EP - 4187

BT - Systems, Man, and Cybernetics (SMC)

PB - IEEE Computer Society

ER -