Abstract
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.
Highlights
Gait recovery is a major rehabilitation goal in post-stroke therapy
For gait cycle related modulations in the 23–40 Hz frequency range a significant interaction between gait phases and conditions was found (F(9, 72) = 2.6, p ≤ 0.0094, η2 = 0.25)(see Table 3)
Our analysis revealed three independent component clusters in premotor and parietal areas that showed significantly decreased spectral power in alpha, beta and 23–40 Hz frequency ranges during the interactive Virtual Environment (VE) tasks compared to MIRROR and GAZE
Summary
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). It has been argued that a more interactive and demanding learning context, might enhance the individual’s motivation and promote active participation in the motor task. Recent studies suggests that VE can promote active participation during robotic gait training. Brütsch et al (2010, 2011) and Schuler et al (2011) showed that training with VE significantly increased active participation during robot assisted gait in children with various neurological gait disorders and healthy controls. Active participation was assessed using biofeedback values from hip and knee torques (Brütsch et al, 2010, 2011) and electromyographic activity of the lower limbs
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