Cheng Ming, Xiaorong Gao, Shangkai Gao, Boliang Wang
{"title":"Stimulation frequency extraction in SSVEP-based brain-computer interface","authors":"Cheng Ming, Xiaorong Gao, Shangkai Gao, Boliang Wang","doi":"10.1109/ICNIC.2005.1499843","DOIUrl":null,"url":null,"abstract":"A periodogram-based method is used to extract the stimulation frequency in a brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). In the system, tens of buttons illuminated at different frequencies are used to generate deterministic sinusoidal responses or SSVEP at the visual cortex, which are derived from the electroencephalogram (EEG) by a suitable electrode array. Based on the periodogram of a time-series, we test the EEG data for the presence of hidden periodic components, which correspond to SSVEP, and extract the stimulation frequencies. The method performs well in simulation, and is applied successfully to real data.","PeriodicalId":169717,"journal":{"name":"Proceedings. 2005 First International Conference on Neural Interface and Control, 2005.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 2005 First International Conference on Neural Interface and Control, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNIC.2005.1499843","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
A periodogram-based method is used to extract the stimulation frequency in a brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). In the system, tens of buttons illuminated at different frequencies are used to generate deterministic sinusoidal responses or SSVEP at the visual cortex, which are derived from the electroencephalogram (EEG) by a suitable electrode array. Based on the periodogram of a time-series, we test the EEG data for the presence of hidden periodic components, which correspond to SSVEP, and extract the stimulation frequencies. The method performs well in simulation, and is applied successfully to real data.