{"title":"EEG data processing with neural network","authors":"T. Majoros, B. Ujvari, S. Oniga","doi":"10.2478/cjece-2019-0014","DOIUrl":null,"url":null,"abstract":"Abstract Machine-learning techniques allow to extract information from electroencephalographic (EEG) recordings of brain activity. By processing the measurement results of a publicly available EEG dataset, we were able to obtain information that could be used to train a feedforward neural network to classify two types of volunteer activities with high efficiency.","PeriodicalId":30888,"journal":{"name":"Carpathian Journal of Electronic and Computer Engineering","volume":"12 1","pages":"33 - 36"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Carpathian Journal of Electronic and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/cjece-2019-0014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Abstract Machine-learning techniques allow to extract information from electroencephalographic (EEG) recordings of brain activity. By processing the measurement results of a publicly available EEG dataset, we were able to obtain information that could be used to train a feedforward neural network to classify two types of volunteer activities with high efficiency.