A preliminary study to assess the brain waves during walking: artifact elimination using soft dynamic time warping

Abstract

Existing electroencephalography (EEG) studies predominantly involve participants in stationary positions, which presents challenges in accurately capturing EEG data during physical activities due to motion-induced noise and artifacts. This study aims to assess and validate the efficacy of the Soft Dynamic Time Warping (Soft-DTW) clustering method for analyzing EEG data collected during physical activity, focusing on an oddball auditory task performed while walking. Employing a mobile active bio-amplifier, the study captures brain activity and assesses auditory event-related potentials (ERPs) under dynamic conditions. The comparative performance of five clustering techniques, k-shape, kernels, k-means, Dynamic Time Warping, and Soft-DTW, in terms of their effectiveness in artifact reduction, was analyzed. Results indicated a significant difference between target and non-target auditory stimuli, with the target stimuli exhibiting a positive (positive) potential, although of smaller magnitude. This outcome suggests that, despite significant artifact interference from walking, Soft-DTW facilitates extracting differences in cognitive processes for the oddball task from the EEG data.