WalkingWizard - 适合日常使用的真正可佩戴脑电图耳机

Teck Lun Goh, L. Peh
{"title":"WalkingWizard - 适合日常使用的真正可佩戴脑电图耳机","authors":"Teck Lun Goh, L. Peh","doi":"10.1145/3648106","DOIUrl":null,"url":null,"abstract":"\n Electroencephalography (EEG) provides an opportunity to gain insights to electrocortical activity without the need for invasive technology. While increasingly used in various application areas, EEG headsets tend to be suited only to a laboratory environment due to the long preparation time to don the headset and the need for users to remain stationary. We present our design of a dry, dual-electrodes flexible PCB assembly that realizes accurate sensing in face of practical motion artifacts. Using it, we present WalkingWizard, our prototype dry-electrode EEG baseball cap that can be used under motion in everyday scenarios. We first evaluated its hardware performance by comparing its electrode-scalp impedance and ability to capture alpha rhythm against both wet EEG, and commercially available dry EEG headsets. We then tested WalkingWizard using SSVEP experiments, achieving high classification accuracy of 87% for walking speeds up to 5.0km/hr, beating state-of-the-art. Expanding on WalkingWizard, we integrated all necessary electronic components into a flexible PCB assembly - realizing\n WalkingWizard Integrated\n , in a truly wearable form-factor. Utilizing WalkingWizard Integrated, we demonstrated several applications as proof-of-concept: Classification of SSVEP in VR environment while walking, Real-time acquisition of emotional state of users while moving around the neighbourhood, and Understanding the effect of guided meditation for relaxation.\n","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"61 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"WalkingWizard - A truly wearable EEG headset for everyday use\",\"authors\":\"Teck Lun Goh, L. Peh\",\"doi\":\"10.1145/3648106\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Electroencephalography (EEG) provides an opportunity to gain insights to electrocortical activity without the need for invasive technology. While increasingly used in various application areas, EEG headsets tend to be suited only to a laboratory environment due to the long preparation time to don the headset and the need for users to remain stationary. We present our design of a dry, dual-electrodes flexible PCB assembly that realizes accurate sensing in face of practical motion artifacts. Using it, we present WalkingWizard, our prototype dry-electrode EEG baseball cap that can be used under motion in everyday scenarios. We first evaluated its hardware performance by comparing its electrode-scalp impedance and ability to capture alpha rhythm against both wet EEG, and commercially available dry EEG headsets. We then tested WalkingWizard using SSVEP experiments, achieving high classification accuracy of 87% for walking speeds up to 5.0km/hr, beating state-of-the-art. Expanding on WalkingWizard, we integrated all necessary electronic components into a flexible PCB assembly - realizing\\n WalkingWizard Integrated\\n , in a truly wearable form-factor. Utilizing WalkingWizard Integrated, we demonstrated several applications as proof-of-concept: Classification of SSVEP in VR environment while walking, Real-time acquisition of emotional state of users while moving around the neighbourhood, and Understanding the effect of guided meditation for relaxation.\\n\",\"PeriodicalId\":72043,\"journal\":{\"name\":\"ACM transactions on computing for healthcare\",\"volume\":\"61 \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM transactions on computing for healthcare\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3648106\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM transactions on computing for healthcare","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3648106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

脑电图(EEG)提供了一个无需侵入性技术即可深入了解皮层电活动的机会。虽然脑电图耳机越来越多地应用于各个领域,但由于佩戴耳机的准备时间较长,而且用户需要保持静止不动,因此往往只适用于实验室环境。我们介绍了我们设计的干式双电极柔性 PCB 组件,它能在实际运动伪影面前实现精确传感。利用它,我们推出了 WalkingWizard,这是我们的干电极脑电图棒球帽原型,可在日常运动场景下使用。我们首先评估了它的硬件性能,将其电极鳞片阻抗和捕捉α节律的能力与湿式脑电图和市售干式脑电图耳机进行了比较。然后,我们使用 SSVEP 实验对 WalkingWizard 进行了测试,在步行速度高达 5.0km/hr 的情况下,分类准确率高达 87%,超过了最先进的水平。在 WalkingWizard 的基础上,我们将所有必要的电子元件集成到一个灵活的印刷电路板组件中--实现了 WalkingWizard Integrated,具有真正的可穿戴外形。利用 WalkingWizard Integrated,我们展示了几个应用作为概念验证:步行时在 VR 环境中对 SSVEP 进行分类、在社区中移动时实时获取用户的情绪状态,以及了解引导式冥想对放松的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
WalkingWizard - A truly wearable EEG headset for everyday use
Electroencephalography (EEG) provides an opportunity to gain insights to electrocortical activity without the need for invasive technology. While increasingly used in various application areas, EEG headsets tend to be suited only to a laboratory environment due to the long preparation time to don the headset and the need for users to remain stationary. We present our design of a dry, dual-electrodes flexible PCB assembly that realizes accurate sensing in face of practical motion artifacts. Using it, we present WalkingWizard, our prototype dry-electrode EEG baseball cap that can be used under motion in everyday scenarios. We first evaluated its hardware performance by comparing its electrode-scalp impedance and ability to capture alpha rhythm against both wet EEG, and commercially available dry EEG headsets. We then tested WalkingWizard using SSVEP experiments, achieving high classification accuracy of 87% for walking speeds up to 5.0km/hr, beating state-of-the-art. Expanding on WalkingWizard, we integrated all necessary electronic components into a flexible PCB assembly - realizing WalkingWizard Integrated , in a truly wearable form-factor. Utilizing WalkingWizard Integrated, we demonstrated several applications as proof-of-concept: Classification of SSVEP in VR environment while walking, Real-time acquisition of emotional state of users while moving around the neighbourhood, and Understanding the effect of guided meditation for relaxation.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
10.30
自引率
0.00%
发文量
0
期刊最新文献
A method for comparing time series by untangling time-dependent and independent variations in biological processes AI-assisted Diagnosing, Monitoring, and Treatment of Mental Disorders: A Survey HEalthRecordBERT (HERBERT): leveraging transformers on electronic health records for chronic kidney disease risk stratification iScan: Detection of Colorectal Cancer From CT Scan Images Using Deep Learning A Computation Model to Estimate Interaction Intensity through Non-verbal Behavioral Cues: A Case Study of Intimate Couples under the Impact of Acute Alcohol Consumption
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1