{"title":"A comparison study of SSVEP detection methods using the Emotiv Epoc headset","authors":"Omar Trigui, W. Zouch, M. B. Ben Messaoud","doi":"10.1109/STA.2015.7505108","DOIUrl":null,"url":null,"abstract":"Recently, the low cost EEG acquisition systems such as the Emotiv Epoc give new tools to develop Brain-Computer interface systems for everyday use outside the laboratory. However, the low sampling rate and the low number of channels remain possible sources of failure. The Canonical Correlation Analysis and the Multivariate Synchronization Index (MSI) methods are applied in a SSVEP-based BCI in order to compare their accuracies. The main goal of this research is to find the appropriate method allowing the control of an autonomous wheelchair by the severely handicapped people. The experimental results show that the MSI method reaches 96% of accuracy with optimal parameters.","PeriodicalId":128530,"journal":{"name":"2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STA.2015.7505108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Recently, the low cost EEG acquisition systems such as the Emotiv Epoc give new tools to develop Brain-Computer interface systems for everyday use outside the laboratory. However, the low sampling rate and the low number of channels remain possible sources of failure. The Canonical Correlation Analysis and the Multivariate Synchronization Index (MSI) methods are applied in a SSVEP-based BCI in order to compare their accuracies. The main goal of this research is to find the appropriate method allowing the control of an autonomous wheelchair by the severely handicapped people. The experimental results show that the MSI method reaches 96% of accuracy with optimal parameters.