http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00093/full?
Johanna Wagner1, Teodoro Solis-Escalante1,2, Reinhold Scherer1,3*, Christa Neuper1,4 and Gernot Müller-Putz1
1Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, BioTechMed, Graz University of Technology, Graz, Austria
2Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
3Rehabilitation Clinic Judendorf-Strassengel, Judendorf-Strassengel, Austria
4Department of Psychology, BioTechMed, University of Graz, Graz, Austria
Voluntary drive is crucial for motor learning, therefore we are interested in the role that motor planning plays in gait movements. In this study we examined the impact of an interactive Virtual Environment (VE) feedback task on the EEG patterns during robot assisted walking. We compared walking in the VE modality to two control conditions: walking with a visual attention paradigm, in which visual stimuli were unrelated to the motor task; and walking with mirror feedback, in which participants observed their own movements. Eleven healthy participants were considered. Application of independent component analysis to the EEG revealed three independent component clusters in premotor and parietal areas showing increased activity during walking with the adaptive VE training paradigm compared to the control conditions. During the interactive VE walking task spectral power in frequency ranges 8–12, 15–20, and 23–40 Hz was significantly (p ≤ 0.05) decreased. This power decrease is interpreted as a correlate of an active cortical area. Furthermore activity in the premotor cortex revealed gait cycle related modulations significantly different (p ≤ 0.05) from baseline in the frequency range 23–40 Hz during walking. These modulations were significantly (p ≤ 0.05) reduced depending on gait cycle phases in the interactive VE walking task compared to the control conditions. We demonstrate that premotor and parietal areas show increased activity during walking with the adaptive VE training paradigm, when compared to walking with mirror- and movement unrelated feedback. Previous research has related a premotor-parietal network to motor planning and motor intention. We argue that movement related interactive feedback enhances motor planning and motor intention. We hypothesize that this might improve gait recovery during rehabilitation.
1. Introduction
Gait recovery is a major rehabilitation goal in post-stroke therapy. Impairments in normal gait affect balance, stride length, walking speed, obstacle avoidance and endurance. These factors often lead to an increased risk of falls and related injuries (Said et al., 1999). In consequence, affected individuals are not able to react adequately and promptly to demands within their environment, which hinders them in performing activities of daily living autonomously (Duncan et al., 1998).
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- 1Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, BioTechMed, Graz University of Technology, Graz, Austria
- 2Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands
- 3Rehabilitation Clinic Judendorf-Strassengel, Judendorf-Strassengel, Austria
- 4Department of Psychology, BioTechMed, University of Graz, Graz, Austria
1. Introduction
Gait recovery is a major rehabilitation goal in
post-stroke therapy. Impairments in normal gait affect balance, stride
length, walking speed, obstacle avoidance and endurance. These factors
often lead to an increased risk of falls and related injuries (Said et al., 1999).
In consequence, affected individuals are not able to react adequately
and promptly to demands within their environment, which hinders them in
performing activities of daily living autonomously (Duncan et al., 1998).
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