{"title":"Online implementation of top-down SSVEP-BMI","authors":"Min-Hee Ahn, Byoung-Kyong Min","doi":"10.1109/IWW-BCI.2017.7858149","DOIUrl":null,"url":null,"abstract":"Brain machine interfaces (BMIs) enable us to control external devices using our brain signals. Using a grid-shaped flickering line-array and a shrink-rLDA classifier, top-down information could recently be decoded in a steady-state visual evoked potential (SSVEP)-based BMI paradigm. The present study tested its feasibility in online implementation. We found that within reasonable computing time (0.114 s on average) its online system was successfully accomplished with a decoding accuracy of 53.7% on average. The accuracy was 3.2 times significantly higher than the accuracy by random-shuffled data (16.7%). Therefore, using the grid-shaped SSVEP-based BMI, one's multiclass (at least 6 classes) intention can be online decoded and subsequently control external devices.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWW-BCI.2017.7858149","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Brain machine interfaces (BMIs) enable us to control external devices using our brain signals. Using a grid-shaped flickering line-array and a shrink-rLDA classifier, top-down information could recently be decoded in a steady-state visual evoked potential (SSVEP)-based BMI paradigm. The present study tested its feasibility in online implementation. We found that within reasonable computing time (0.114 s on average) its online system was successfully accomplished with a decoding accuracy of 53.7% on average. The accuracy was 3.2 times significantly higher than the accuracy by random-shuffled data (16.7%). Therefore, using the grid-shaped SSVEP-based BMI, one's multiclass (at least 6 classes) intention can be online decoded and subsequently control external devices.
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