基于特征描述子模式的心理旋转活动分析研究

Sayantani Ghosh, Lidia Ghosh, A. Konar
{"title":"基于特征描述子模式的心理旋转活动分析研究","authors":"Sayantani Ghosh, Lidia Ghosh, A. Konar","doi":"10.1109/WISPNET.2018.8538540","DOIUrl":null,"url":null,"abstract":"The chief objective of this paper is to investigate the differences in Feature Descriptor patterns during mental rotation of five different objects for both healthy as well as brain diseased subjects. Thus, electroencephalographic (EEG) activity was measured during mental rotation of objects by various angles with respect to its present orientation. Source localization using eLORETA inferred an enhanced activation of pre-frontal and frontal lobe regions during mental rotation activity. Experimental analysis also confirmed maximal activation of lower alpha frequency band while performing this cognitive task. Differential Evolutionary (DE) algorithm has been implemented to select the optimal features which are represented using the Feature Descriptor diagrams. These diagrams infer that the feature patterns are distinct and vary from object to object. Moreover, these patterns orient by $45^{\\mathrm {o}}$ for $90^{\\mathrm {o}}$ mental rotation and by $75^{\\mathrm {o}}$ for $180^{\\mathrm {o}}$ mental rotation of the presented objects. However, there exists an inconsistency in the Feature descriptor diagrams for patients suffering from pre-frontal lobe amnesia and Alzheimer's disease. It is also found that these diagrams remain unaffected during mental rotation which infers their incapability to perform such a cognitive task. Hence, this work can be effectively utilized to detect people suffering from memory related disorder.","PeriodicalId":6858,"journal":{"name":"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)","volume":"2000 1","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Analytical Study of Mental Rotation Activity Based on Feature Descriptor Patterns\",\"authors\":\"Sayantani Ghosh, Lidia Ghosh, A. Konar\",\"doi\":\"10.1109/WISPNET.2018.8538540\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The chief objective of this paper is to investigate the differences in Feature Descriptor patterns during mental rotation of five different objects for both healthy as well as brain diseased subjects. Thus, electroencephalographic (EEG) activity was measured during mental rotation of objects by various angles with respect to its present orientation. Source localization using eLORETA inferred an enhanced activation of pre-frontal and frontal lobe regions during mental rotation activity. Experimental analysis also confirmed maximal activation of lower alpha frequency band while performing this cognitive task. Differential Evolutionary (DE) algorithm has been implemented to select the optimal features which are represented using the Feature Descriptor diagrams. These diagrams infer that the feature patterns are distinct and vary from object to object. Moreover, these patterns orient by $45^{\\\\mathrm {o}}$ for $90^{\\\\mathrm {o}}$ mental rotation and by $75^{\\\\mathrm {o}}$ for $180^{\\\\mathrm {o}}$ mental rotation of the presented objects. However, there exists an inconsistency in the Feature descriptor diagrams for patients suffering from pre-frontal lobe amnesia and Alzheimer's disease. It is also found that these diagrams remain unaffected during mental rotation which infers their incapability to perform such a cognitive task. Hence, this work can be effectively utilized to detect people suffering from memory related disorder.\",\"PeriodicalId\":6858,\"journal\":{\"name\":\"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)\",\"volume\":\"2000 1\",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WISPNET.2018.8538540\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WISPNET.2018.8538540","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

本文的主要目的是研究健康和脑病变受试者在五种不同物体的心理旋转过程中特征描述子模式的差异。因此,脑电图(EEG)活动是在相对于物体当前方向的不同角度的心理旋转过程中测量的。使用eLORETA进行源定位推断,在心理旋转活动中,前额叶和额叶区域的激活增强。实验分析也证实了在执行这一认知任务时,较低α频带的最大激活。采用差分进化算法选择最优特征,并用特征描述符图表示最优特征。这些图推断出特征模式是不同的,并且因对象而异。此外,对于呈现对象的$90^{\ mathm {o}}$心理旋转,这些模式以$45^{\ mathm {o}}$定向;对于$180^{\ mathm {o}}$心理旋转,这些模式以$75^{\ mathm {o}}$定向。然而,前额叶失忆症和阿尔茨海默病患者的特征描述符图存在不一致。研究还发现,在心理旋转过程中,这些图表不受影响,这推断出他们无法执行这样的认知任务。因此,这项工作可以有效地用于检测患有记忆相关障碍的人。
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
An Analytical Study of Mental Rotation Activity Based on Feature Descriptor Patterns
The chief objective of this paper is to investigate the differences in Feature Descriptor patterns during mental rotation of five different objects for both healthy as well as brain diseased subjects. Thus, electroencephalographic (EEG) activity was measured during mental rotation of objects by various angles with respect to its present orientation. Source localization using eLORETA inferred an enhanced activation of pre-frontal and frontal lobe regions during mental rotation activity. Experimental analysis also confirmed maximal activation of lower alpha frequency band while performing this cognitive task. Differential Evolutionary (DE) algorithm has been implemented to select the optimal features which are represented using the Feature Descriptor diagrams. These diagrams infer that the feature patterns are distinct and vary from object to object. Moreover, these patterns orient by $45^{\mathrm {o}}$ for $90^{\mathrm {o}}$ mental rotation and by $75^{\mathrm {o}}$ for $180^{\mathrm {o}}$ mental rotation of the presented objects. However, there exists an inconsistency in the Feature descriptor diagrams for patients suffering from pre-frontal lobe amnesia and Alzheimer's disease. It is also found that these diagrams remain unaffected during mental rotation which infers their incapability to perform such a cognitive task. Hence, this work can be effectively utilized to detect people suffering from memory related disorder.
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
自引率
0.00%
发文量
0
期刊最新文献
Deep Reinforcement Learning for the Capacitated Vehicle Routing Problem with Soft Time Window Integrated Interference Solutions Between 5G and Satellite Systems Modulation Recognition Method of MAPSK Signal Artificial Intelligence Routing Method in Wireless Sensor Network for Sewage Treatment Monitoring Electromagnetically Induced Transparency in a Coupled NV Spin-Mechanical Resonator System
×
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