Teng Limin, Shuntaro Hatori, Shunsuke Fukushi, Xing Yi, Kota Chiba, Yoritaka Akimoto, Takashi Yamaguchi, Yuta Nishiyama, Shusaku Nomura, E. A. Chayani Dilrukshi
{"title":"A preliminary study to assess the brain waves during walking: artifact elimination using soft dynamic time warping","authors":"Teng Limin, Shuntaro Hatori, Shunsuke Fukushi, Xing Yi, Kota Chiba, Yoritaka Akimoto, Takashi Yamaguchi, Yuta Nishiyama, Shusaku Nomura, E. A. Chayani Dilrukshi","doi":"10.1007/s10015-024-00981-4","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 1","pages":"136 - 142"},"PeriodicalIF":0.8000,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Life and Robotics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s10015-024-00981-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
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.
现有的脑电图(EEG)研究主要涉及静止位置的参与者,由于运动引起的噪声和伪影,这给准确捕获身体活动期间的EEG数据带来了挑战。本研究旨在评估和验证软动态时间扭曲(Soft- Dynamic Time Warping, Soft- dtw)聚类方法在分析身体活动时收集的脑电数据的有效性,并以行走时执行的古怪听觉任务为研究对象。该研究采用移动有源生物放大器,在动态条件下捕捉大脑活动并评估听觉事件相关电位(erp)。对比分析了k-shape、kernel、k-means、Dynamic Time Warping和Soft-DTW五种聚类技术在减少伪影方面的效果。结果表明,目标和非目标听觉刺激之间存在显著差异,目标刺激表现出正(正)电位,尽管量级较小。这一结果表明,尽管行走产生了明显的伪影干扰,但软dtw有助于从脑电图数据中提取古怪任务的认知过程差异。