{"title":"事件相关电位分类算法的比较","authors":"A. Alsufyani","doi":"10.1109/CAIPT.2017.8320734","DOIUrl":null,"url":null,"abstract":"We compare six methods for classifying low signal-to-noise Event Related Potential (ERP) data into deceiving and nondeceiving. Three methods perform poorly: Correlation Coefficient (CD), Support Vector Machine with linear Kernel (LSVM) and SVM with radial basis kernel (RBSVM), while another, Amplitude Difference (AD), performs satisfactorily. However, a simple by-individual weight template method performs better still.","PeriodicalId":351075,"journal":{"name":"2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A comparison of classification algorithms for Event Related Potentials\",\"authors\":\"A. Alsufyani\",\"doi\":\"10.1109/CAIPT.2017.8320734\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We compare six methods for classifying low signal-to-noise Event Related Potential (ERP) data into deceiving and nondeceiving. Three methods perform poorly: Correlation Coefficient (CD), Support Vector Machine with linear Kernel (LSVM) and SVM with radial basis kernel (RBSVM), while another, Amplitude Difference (AD), performs satisfactorily. However, a simple by-individual weight template method performs better still.\",\"PeriodicalId\":351075,\"journal\":{\"name\":\"2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAIPT.2017.8320734\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAIPT.2017.8320734","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A comparison of classification algorithms for Event Related Potentials
We compare six methods for classifying low signal-to-noise Event Related Potential (ERP) data into deceiving and nondeceiving. Three methods perform poorly: Correlation Coefficient (CD), Support Vector Machine with linear Kernel (LSVM) and SVM with radial basis kernel (RBSVM), while another, Amplitude Difference (AD), performs satisfactorily. However, a simple by-individual weight template method performs better still.
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