{"title":"基于MRP的脑机接口半监督时空滤波","authors":"Jun Lv, Lei Wang","doi":"10.1109/ICINFA.2011.5949048","DOIUrl":null,"url":null,"abstract":"In brain-computer interface (BCI) studies, if the number of training trails is small, the discriminative patterns of movement related potentials (MRPs) can not be appropriately extracted by temporal-spatial filter (TSF) algorithm. Thus in this paper, we proposed a semi-supervised TSF (ssTSF) algorithm which employed self-training scheme to induce the unlabelled trails with high confidences and learn the discriminative patterns of MRPs iteratively. We compared TSF and ssTSF algorithm on the data from BCI competition I. The results demonstrated the effectiveness of the ssTSF, especially for small training sets.","PeriodicalId":299418,"journal":{"name":"2011 IEEE International Conference on Information and Automation","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Semi-supervised temporal-spatial filter based on MRP for brain-computer interfaces\",\"authors\":\"Jun Lv, Lei Wang\",\"doi\":\"10.1109/ICINFA.2011.5949048\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In brain-computer interface (BCI) studies, if the number of training trails is small, the discriminative patterns of movement related potentials (MRPs) can not be appropriately extracted by temporal-spatial filter (TSF) algorithm. Thus in this paper, we proposed a semi-supervised TSF (ssTSF) algorithm which employed self-training scheme to induce the unlabelled trails with high confidences and learn the discriminative patterns of MRPs iteratively. We compared TSF and ssTSF algorithm on the data from BCI competition I. The results demonstrated the effectiveness of the ssTSF, especially for small training sets.\",\"PeriodicalId\":299418,\"journal\":{\"name\":\"2011 IEEE International Conference on Information and Automation\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE International Conference on Information and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICINFA.2011.5949048\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Information and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2011.5949048","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Semi-supervised temporal-spatial filter based on MRP for brain-computer interfaces
In brain-computer interface (BCI) studies, if the number of training trails is small, the discriminative patterns of movement related potentials (MRPs) can not be appropriately extracted by temporal-spatial filter (TSF) algorithm. Thus in this paper, we proposed a semi-supervised TSF (ssTSF) algorithm which employed self-training scheme to induce the unlabelled trails with high confidences and learn the discriminative patterns of MRPs iteratively. We compared TSF and ssTSF algorithm on the data from BCI competition I. The results demonstrated the effectiveness of the ssTSF, especially for small training sets.