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Updated: Jan 20, 2026

Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients
Published on: March 11, 2021
A new approach to quantifying the EEG during walking: Initial evidence of gait related potentials and their changes
I Maidan1, D Patashov2, S Shustak3
1Laboratory for Early Markers of Neurodegeneration, Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler School of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
Background:
The electroencephalogram (EEG) can be a useful tool to investigate the neurophysiology of gait during walking. Our aims were to develop an approach that identify and quantify event related potentials (ERPs) during a gait cycle and to examine the effects of aging and dual tasking on these gait related potentials (GRPs).
Methods:
10 young and 10 older adults walked on a treadmill while wearing a wireless 20-channels EEG and accelerometers on the ankles. Each heel strike extracted from the accelerometers was used as an event to which the electrical brain activity pattern was locked. The subjects performed usual and dual task walking that included an auditory oddball task. GRPs amplitude and latency were computed, and a new measure referred to as Amplitude Pattern Consistency (APC) was developed to quantify the consistency of these GRP amplitudes within a gait cycle. The results were compared between and within groups using linear mixed model analysis.
Results:
The electrical pattern during a gait cycle consisted of two main positive GRPs. Differences in these GRPs between young and older adults were observed in Pz and Cz. In Pz, older adults had higher GRPs amplitude (p = 0.006, p = 0.010), and in Cz lower APC (p = 0.025). Alterations were also observed between the walking tasks. Both groups showed shorter latency during oddball walking compared to usual walking in Cz (p = 0.040). In addition, the APC in Cz was correlated with gait speed (r = 0.599, p = 0.011) in all subjects and with stride time variability in the older adults (r = -0.703, p = 0.023).
Conclusions:
This study is the first to define specific gait related potentials within a gait cycle using novel methods for quantifying waveforms. Our findings show the potential of this approach to be applied broadly to study the EEG during gait in a variety of contexts. The observed changes in GRPs with aging and walking task and the relationship between GRPs and gait may suggest the neurophysiologic foundation for studying walking and for developing new approaches for improving gait.
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