{"title":"Influence of differential features in focal and non-focal EEG signal classification","authors":"O. K. Fasil, R. Rajesh, T. M. Thasleema","doi":"10.1109/R10-HTC.2017.8289042","DOIUrl":null,"url":null,"abstract":"Electroencephalogram (EEG) is the best bio-medical modality to capture the brain functions because of its abundant availability at low cost. The recent development of methods for feature extraction and classification has led to the improved understanding of the brain functions, better diagnosis and treatment of brain disorders. This paper focuses on the influences of differential features in focal and non-focal EEG epilepsy classification. Experimental results depicts that the capability of differential features in classification of epileptic EEG signals is promising.","PeriodicalId":411099,"journal":{"name":"2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/R10-HTC.2017.8289042","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
Electroencephalogram (EEG) is the best bio-medical modality to capture the brain functions because of its abundant availability at low cost. The recent development of methods for feature extraction and classification has led to the improved understanding of the brain functions, better diagnosis and treatment of brain disorders. This paper focuses on the influences of differential features in focal and non-focal EEG epilepsy classification. Experimental results depicts that the capability of differential features in classification of epileptic EEG signals is promising.