EEG-Based Perceived Tactile Location Prediction

Deng Wang, Yadong Liu, D. Hu, Gunnar Blohm
{"title":"EEG-Based Perceived Tactile Location Prediction","authors":"Deng Wang, Yadong Liu, D. Hu, Gunnar Blohm","doi":"10.1109/TAMD.2015.2427581","DOIUrl":null,"url":null,"abstract":"Previous studies have attempted to investigate the peripheral neural mechanisms implicated in tactile perception, but the neurophysiological data in humans involved in tactile spatial location perception to help the brain orient the body and interact with its surroundings are not well understood. In this paper, we use single-trial electroencephalogram (EEG) measurements to explore the perception of tactile stimuli located on participants' right forearm, which were approximately equally spaced centered on the body midline, 2 leftward and 2 rightward of midline. An EEG-based signal analysis approach to predict the location of the tactile stimuli is proposed. Offline classification suggests that tactile location can be detected from EEG signals in single trial (four-class classifier for location discriminate can achieve up to 96.76%) with a short response time (600 milliseconds after stimulus presentation). From a human-machine-interaction (HMI) point of view, this could be used to design a real-time reactive control machine for patients, e.g., suffering from hypoesthesia.","PeriodicalId":49193,"journal":{"name":"IEEE Transactions on Autonomous Mental Development","volume":"7 1","pages":"342-348"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TAMD.2015.2427581","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Autonomous Mental Development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAMD.2015.2427581","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

Previous studies have attempted to investigate the peripheral neural mechanisms implicated in tactile perception, but the neurophysiological data in humans involved in tactile spatial location perception to help the brain orient the body and interact with its surroundings are not well understood. In this paper, we use single-trial electroencephalogram (EEG) measurements to explore the perception of tactile stimuli located on participants' right forearm, which were approximately equally spaced centered on the body midline, 2 leftward and 2 rightward of midline. An EEG-based signal analysis approach to predict the location of the tactile stimuli is proposed. Offline classification suggests that tactile location can be detected from EEG signals in single trial (four-class classifier for location discriminate can achieve up to 96.76%) with a short response time (600 milliseconds after stimulus presentation). From a human-machine-interaction (HMI) point of view, this could be used to design a real-time reactive control machine for patients, e.g., suffering from hypoesthesia.
基于脑电图的感知触觉位置预测
以往的研究试图探讨触觉感知中涉及的外周神经机制,但人类参与触觉空间定位感知以帮助大脑定位身体并与周围环境相互作用的神经生理学数据尚未得到很好的理解。在本文中,我们使用单次脑电图(EEG)测量来探索位于被试右前臂的触觉刺激的感知,这些触觉刺激以身体中线为中心,在中线的左侧和右侧各2个,大约等距。提出了一种基于脑电图信号分析的触觉刺激位置预测方法。离线分类表明,单次实验可以在较短的响应时间内(刺激呈现后600毫秒)从脑电信号中检测到触觉位置(四类分类器的位置判别准确率高达96.76%)。从人机交互(HMI)的角度来看,这可以用于为患者设计实时反应性控制机器,例如,患有感觉减退的患者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Transactions on Autonomous Mental Development
IEEE Transactions on Autonomous Mental Development COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ROBOTICS
自引率
0.00%
发文量
0
审稿时长
3 months
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信