A Survey on EEG Data Analysis Software

Decis. Sci. Pub Date : 2023-06-01 DOI:10.3390/sci5020023
Rupak Kumar Das, Anna Martin, Tom Zurales, Dale Dowling, Arshia A. Khan
{"title":"A Survey on EEG Data Analysis Software","authors":"Rupak Kumar Das, Anna Martin, Tom Zurales, Dale Dowling, Arshia A. Khan","doi":"10.3390/sci5020023","DOIUrl":null,"url":null,"abstract":"Electroencephalography (EEG) is a mechanism to understand the brain’s functioning by analyzing brain electrical signals. More recently, it has been more commonly used in studies that are focused on the causation and effect of dementia. More tools are now available to gather EEG data. This brings about the challenge of understanding brain signals, which involves signal processing. Professionals with an electrical engineering background are very comfortable analyzing EEG data. Still, scientists in computer science and related fields need a source that can identify all the tools available and the process of analyzing the data. This paper deals specifically with the existing EEG data analysis tools and the processes involved in analyzing the EEG data using these tools. Furthermore, the paper goes in-depth into identifying the tools and the mechanisms of data processing techniques. In addition, it lists a set of definitions required for a better understanding of EEG data analysis, which can be challenging. The purpose of this paper is to serve as a reference for not only scientists that are new to EEG data analysis but also seasoned scientists that are looking for a specific data component in EEG and can go straight to the section of the paper that deals with the tool that they are using.","PeriodicalId":10987,"journal":{"name":"Decis. Sci.","volume":"26 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decis. Sci.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/sci5020023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Electroencephalography (EEG) is a mechanism to understand the brain’s functioning by analyzing brain electrical signals. More recently, it has been more commonly used in studies that are focused on the causation and effect of dementia. More tools are now available to gather EEG data. This brings about the challenge of understanding brain signals, which involves signal processing. Professionals with an electrical engineering background are very comfortable analyzing EEG data. Still, scientists in computer science and related fields need a source that can identify all the tools available and the process of analyzing the data. This paper deals specifically with the existing EEG data analysis tools and the processes involved in analyzing the EEG data using these tools. Furthermore, the paper goes in-depth into identifying the tools and the mechanisms of data processing techniques. In addition, it lists a set of definitions required for a better understanding of EEG data analysis, which can be challenging. The purpose of this paper is to serve as a reference for not only scientists that are new to EEG data analysis but also seasoned scientists that are looking for a specific data component in EEG and can go straight to the section of the paper that deals with the tool that they are using.
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
脑电数据分析软件综述
脑电图(EEG)是一种通过分析脑电信号来了解大脑功能的机制。最近,它更常用于关注痴呆症的因果关系的研究中。现在有更多的工具可用于收集脑电图数据。这就带来了理解大脑信号的挑战,这涉及到信号处理。具有电气工程背景的专业人员非常擅长分析脑电图数据。尽管如此,计算机科学和相关领域的科学家需要一个能够识别所有可用工具和分析数据过程的来源。本文详细介绍了现有的脑电数据分析工具以及使用这些工具分析脑电数据所涉及的过程。此外,本文还深入探讨了数据处理技术的工具和机制。此外,它还列出了更好地理解EEG数据分析所需的一组定义,这可能具有挑战性。本文的目的不仅是为刚接触脑电图数据分析的科学家提供参考,也为那些在脑电图中寻找特定数据成分的资深科学家提供参考,他们可以直接进入论文中处理他们正在使用的工具的部分。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Privacy and Security of Blockchain in Healthcare: Applications, Challenges, and Future Perspectives Digital Twins in Manufacturing: A RAMI 4.0 Compliant Concept In Silico Study of Potential Small Molecule TIPE2 Inhibitors for the Treatment of Cancer Treatment of Diabetes Mellitus by Acupuncture: Dynamics of Blood Glucose Level and Its Mathematical Modelling T5 for Hate Speech, Augmented Data, and Ensemble
×
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