Yunyong Punsawad, Nannaphat Siribunyaphat, Y. Wongsawat
{"title":"Self-Flickering Visual Stimulus based on Visual illusion for SSVEP-based BCI System","authors":"Yunyong Punsawad, Nannaphat Siribunyaphat, Y. Wongsawat","doi":"10.1109/BMEICON.2018.8610000","DOIUrl":null,"url":null,"abstract":"This paper proposes the use of the windmill pattern visual stimulus to induce human vision by employing a phenomenon of visual illusion for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. We had to explore the brain response to the flickering pattern as windmill pattern, three BCI commands can be generated by using three different windmill patterns. SSVEP technique was used to detect the response. The average accuracy of classification was approximately 80.5%. With the proposed visual stimulus pattern, it can reduce eye fatigue and increase the number of commands for the existing SSVEP-based BCI. Therefore, the proposed visual stimulus pattern can be used for practical BCI applications","PeriodicalId":232271,"journal":{"name":"2018 11th Biomedical Engineering International Conference (BMEiCON)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 11th Biomedical Engineering International Conference (BMEiCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BMEICON.2018.8610000","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
This paper proposes the use of the windmill pattern visual stimulus to induce human vision by employing a phenomenon of visual illusion for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. We had to explore the brain response to the flickering pattern as windmill pattern, three BCI commands can be generated by using three different windmill patterns. SSVEP technique was used to detect the response. The average accuracy of classification was approximately 80.5%. With the proposed visual stimulus pattern, it can reduce eye fatigue and increase the number of commands for the existing SSVEP-based BCI. Therefore, the proposed visual stimulus pattern can be used for practical BCI applications