情绪从消极兴奋状态到积极未兴奋状态的转变：ERP视角

IF 1 4区工程技术 Q4 INSTRUMENTS & INSTRUMENTATION

Metrology and Measurement Systems Pub Date : 2023-07-20 DOI:10.24425/mms.2022.140030

M. Singh, Mandeep Singh

{"title":"情绪从消极兴奋状态到积极未兴奋状态的转变：ERP视角","authors":"M. Singh, Mandeep Singh","doi":"10.24425/mms.2022.140030","DOIUrl":null,"url":null,"abstract":"The cognitive aspects like perception, problem-solving, thinking, task performance, etc., are immensely inﬂuenced by emotions making it necessary to study emotions. The best state of emotion is the positive unexcitedstate,alsoknownastheHighValenceLowArousal(HVLA)stateoftheemotion.Thepsychologists endeavourtobringthesubjectsfromanegativelyexcitedstateofemotion(LowValenceHighArousalstate) toapositiveunexcitedstateofemotion(HighValenceLowArousalstate).Intheﬁrstpartofthisstudy, afour-classsubjectindependentemotionclassiﬁerwasdevelopedwithanSVMpolynomialclassiﬁer usingaverageEventRelatedPotential(ERP)anddiﬀerentialaverageERPattributes.Thevisuallyevoked Electroencephalogram(EEG)signalswereacquiredfrom24subjects.Thefour-classclassiﬁcationaccuracy was83%usingaverageERPattributesand77%usingdiﬀerentialaverageERPattributes.Inthesecond partofthestudy,themeditativeinterventionwasappliedto20subjectswhodeclaredthemselvesnegatively excited(inLowValenceHighArousalstateofemotion).TheEEGsignalswereacquiredbeforeandafter themeditativeintervention.Thefour-classsubjectindependentemotionclassiﬁerdevelopedinStudy1 correctlyclassiﬁedthese20subjectstobeinanegativelyexcitedstateofemotion.Aftertheintervention,16 subjectsself-assessedthemselvestobeinapositiveunexcited(HVLA)stateofemotion(whichshowsthe interventionaccuracyof80%).Testingafour-classsubjectindependentemotionclassiﬁerontheEEGdata acquiredafterthemeditativeinterventionvalidated13of16subjectsinapositiveunexcitedstate,yielding anaccuracyof81.3%.","PeriodicalId":18394,"journal":{"name":"Metrology and Measurement Systems","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Transition of emotions from the negatively excited state to positive unexcited state: an ERP perspective\",\"authors\":\"M. Singh, Mandeep Singh\",\"doi\":\"10.24425/mms.2022.140030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The cognitive aspects like perception, problem-solving, thinking, task performance, etc., are immensely inﬂuenced by emotions making it necessary to study emotions. The best state of emotion is the positive unexcitedstate,alsoknownastheHighValenceLowArousal(HVLA)stateoftheemotion.Thepsychologists endeavourtobringthesubjectsfromanegativelyexcitedstateofemotion(LowValenceHighArousalstate) toapositiveunexcitedstateofemotion(HighValenceLowArousalstate).Intheﬁrstpartofthisstudy, afour-classsubjectindependentemotionclassiﬁerwasdevelopedwithanSVMpolynomialclassiﬁer usingaverageEventRelatedPotential(ERP)anddiﬀerentialaverageERPattributes.Thevisuallyevoked Electroencephalogram(EEG)signalswereacquiredfrom24subjects.Thefour-classclassiﬁcationaccuracy was83%usingaverageERPattributesand77%usingdiﬀerentialaverageERPattributes.Inthesecond partofthestudy,themeditativeinterventionwasappliedto20subjectswhodeclaredthemselvesnegatively excited(inLowValenceHighArousalstateofemotion).TheEEGsignalswereacquiredbeforeandafter themeditativeintervention.Thefour-classsubjectindependentemotionclassiﬁerdevelopedinStudy1 correctlyclassiﬁedthese20subjectstobeinanegativelyexcitedstateofemotion.Aftertheintervention,16 subjectsself-assessedthemselvestobeinapositiveunexcited(HVLA)stateofemotion(whichshowsthe interventionaccuracyof80%).Testingafour-classsubjectindependentemotionclassiﬁerontheEEGdata acquiredafterthemeditativeinterventionvalidated13of16subjectsinapositiveunexcitedstate,yielding anaccuracyof81.3%.\",\"PeriodicalId\":18394,\"journal\":{\"name\":\"Metrology and Measurement Systems\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metrology and Measurement Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.24425/mms.2022.140030\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metrology and Measurement Systems","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.24425/mms.2022.140030","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}

引用次数: 0

摘要

认知方面，如感知、解决问题、思考、任务执行等，都受到情绪的极大影响，因此有必要研究情绪。情绪的最佳状态是积极的未激发状态，也被称为高价低兴奋（HVLA）运动状态。心理学家们努力将受试者的消极情绪从兴奋的运动状态（低价高兴奋状态）转变为积极的未激励运动状态（高价低激动状态）。在本研究的第一部分，SVM多项式类使用平均事件相关电位（ERP）和不同的平均ERPatt值开发了四个与类别受试者无关的运动类别。从24名受试者中获得了可视化脑电图（EEG）信号。使用平均ERPat值时，四个类别的定位准确率为83%，使用不同的平均er值时，准确率为77%。在第二部分研究中，将编辑性干预应用于20名受试者，这些受试者被认为是负兴奋的（在运动的低价高Arousalstate中）。在编辑性干预前后重新获得EEG信号。研究中开发的四类独立于受试者的运动类别1正确的类别使这20名受试者处于负运动状态。干预后，16名受试者被评估为处于积极的未激发（HVLA）运动状态（干预准确率为80%）。在编辑干预后获得的EEG数据中，对四类受试者独立运动状态进行了验证，16名被试中有13名处于积极的非激发状态，产生了81.3%的准确率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Transition of emotions from the negatively excited state to positive unexcited state: an ERP perspective

The cognitive aspects like perception, problem-solving, thinking, task performance, etc., are immensely inﬂuenced by emotions making it necessary to study emotions. The best state of emotion is the positive unexcitedstate,alsoknownastheHighValenceLowArousal(HVLA)stateoftheemotion.Thepsychologists endeavourtobringthesubjectsfromanegativelyexcitedstateofemotion(LowValenceHighArousalstate) toapositiveunexcitedstateofemotion(HighValenceLowArousalstate).Intheﬁrstpartofthisstudy, afour-classsubjectindependentemotionclassiﬁerwasdevelopedwithanSVMpolynomialclassiﬁer usingaverageEventRelatedPotential(ERP)anddiﬀerentialaverageERPattributes.Thevisuallyevoked Electroencephalogram(EEG)signalswereacquiredfrom24subjects.Thefour-classclassiﬁcationaccuracy was83%usingaverageERPattributesand77%usingdiﬀerentialaverageERPattributes.Inthesecond partofthestudy,themeditativeinterventionwasappliedto20subjectswhodeclaredthemselvesnegatively excited(inLowValenceHighArousalstateofemotion).TheEEGsignalswereacquiredbeforeandafter themeditativeintervention.Thefour-classsubjectindependentemotionclassiﬁerdevelopedinStudy1 correctlyclassiﬁedthese20subjectstobeinanegativelyexcitedstateofemotion.Aftertheintervention,16 subjectsself-assessedthemselvestobeinapositiveunexcited(HVLA)stateofemotion(whichshowsthe interventionaccuracyof80%).Testingafour-classsubjectindependentemotionclassiﬁerontheEEGdata acquiredafterthemeditativeinterventionvalidated13of16subjectsinapositiveunexcitedstate,yielding anaccuracyof81.3%.

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

Metrology and Measurement Systems INSTRUMENTS & INSTRUMENTATION-

CiteScore

2.00

自引率

10.00%

发文量

审稿时长

6 months

期刊介绍： Contributions are invited on all aspects of the research, development and applications of the measurement science and technology. The list of topics covered includes: theory and general principles of measurement; measurement of physical, chemical and biological quantities; medical measurements; sensors and transducers; measurement data acquisition; measurement signal transmission; processing and data analysis; measurement systems and embedded systems; design, manufacture and evaluation of instruments. The average publication cycle is 6 months.