Differential gait adaptation patterns in Parkinson's disease - a split belt treadmill pilot study.

BACKGROUND: Interventions using split belt treadmills (SBTM) aim to improve gait symmetry (GA) in Parkinson's disease (PD). Comparative effects in conjugated SBTM conditions were not studied systematically despite potentially affecting intervention outcomes. We compared gait adaptation effects instigated by SBTM walking with respect to the type (increased\decreased speed) and the side (more/less affected) of the manipulated belt in PD. METHODS: Eight individuals with PD performed four trials of SBTM walking, each consisted of baseline tied belt configuration, followed by split belt setting - either WS or BS belt's speed increased or decreased by 50% from baseline, and final tied belt configuration. Based on the disease's motor symptoms, a 'worst' side (WS) and a 'best' side (BS) were defined for each participant. RESULTS: SB initial change in GA was significant regardless of condition (p ≤ 0.02). This change was however more pronounced for BS-decrease compared with its matching condition WS-increase (p = 0.016). Similarly, the same was observed for WS-decrease compared to BS-increase (p = 0.013). Upon returning to tied belt condition, both BS-decrease and WS-increased resulted in a significant change in GA (p = 0.04). Upper limb asymmetry followed a similar trend of GA reversal, although non-significant. CONCLUSIONS: Stronger effects on GA were obtained by decreasing the BS belt's speed of the best side, rather than increasing the speed of the worst side. Albeit a small sample size, which limits the generalisability of these results, we propose that future clinical studies would benefit from considering such methodological planning of SBTM intervention, for maximising of intervention outcomes. Larger samples may reveal arm swinging asymmetries alterations to match SBTM adaptation patterns. Finally, further research is warranted to study post-adaption effects in order to define optimal adaptation schemes to maximise the therapeutic effect of SBTM based interventions.

Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion.

The appreciation for the need to record electroencephalographic (EEG) signals from humans while walking has been steadily growing in recent years, particularly in relation to understanding gait disturbances. Movement artefacts (MA) in EEG signals originate from mechanical forces applied to the scalp electrodes, inducing small electrode movements relative to the scalp which, in turn, cause the recorded voltage to change irrespectively of cortical activity. These mechanical forces, and thus MA, may have various sources (e.g., ground reaction forces, head movements, etc.) that are inherent to daily activities, notably walking. In this paper we introduce a systematic, integrated methodology for removing MA from EEG signals recorded during treadmill (TM) and over-ground (OG) walking, as well as quantify the prevalence of MA in different locomotion settings. In our experiments, participants performed walking trials at various speeds both OG and on a TM while wearing a 32-channel EEG cap and a 3-axis accelerometer, placed on the forehead. Data preprocessing included separating the EEG signals into statistically independent additive components using independent component analysis (ICA). We observed an increase in electro-physiological signals (e.g., neck EMG activations for stabilizing the head during heel-strikes) as the walking speed increased. These artefact independent-components (ICs), while not originating from electrode movement, still exhibit a similar spectral pattern to the MA ICs-a peak at the stepping frequency. MA was identified and quantified in each component using a novel method that utilizes the participant's stepping frequency, derived from a forehead-mounted accelerometer. We then benchmarked the EEG data by applying newly established metrics to quantify the success of our method in cleaning the data. The results indicate that our approach can be successfully applied to EEG data recorded during TM and OG walking, and is offered as a unified methodology for MA removal from EEG collected during gait trials.