{"title":"基于支持向量机的脑电分类预测人体运动意图","authors":"Kaiyang Li, Xiaodong Zhang, Yuhuan Du","doi":"10.1109/URAI.2013.6677297","DOIUrl":null,"url":null,"abstract":"In this paper, the EEG (electroencephalograph) signal acquisition equipment is used to collect the EEG signal of human lower limb movement intention. This paper firstly analyzes α waveform and β waveform, which can most reveal the intentions of human body movement. Then, wavelet transform is used for noise removal, filter and feature extraction. This paper also has described the theory of Support Vector Machine (SVM), and one-to-one SVM method is used for the classification of EEG of six different movement patterns. Finally through the experimental verification, the validity of the proposed research method is demonstrated. The experiment has shown a better judging result, in which the average recognition rate is 78.9%.","PeriodicalId":431699,"journal":{"name":"2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A SVM based classification of EEG for predicting the movement intent of human body\",\"authors\":\"Kaiyang Li, Xiaodong Zhang, Yuhuan Du\",\"doi\":\"10.1109/URAI.2013.6677297\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the EEG (electroencephalograph) signal acquisition equipment is used to collect the EEG signal of human lower limb movement intention. This paper firstly analyzes α waveform and β waveform, which can most reveal the intentions of human body movement. Then, wavelet transform is used for noise removal, filter and feature extraction. This paper also has described the theory of Support Vector Machine (SVM), and one-to-one SVM method is used for the classification of EEG of six different movement patterns. Finally through the experimental verification, the validity of the proposed research method is demonstrated. The experiment has shown a better judging result, in which the average recognition rate is 78.9%.\",\"PeriodicalId\":431699,\"journal\":{\"name\":\"2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)\",\"volume\":\"80 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/URAI.2013.6677297\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/URAI.2013.6677297","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A SVM based classification of EEG for predicting the movement intent of human body
In this paper, the EEG (electroencephalograph) signal acquisition equipment is used to collect the EEG signal of human lower limb movement intention. This paper firstly analyzes α waveform and β waveform, which can most reveal the intentions of human body movement. Then, wavelet transform is used for noise removal, filter and feature extraction. This paper also has described the theory of Support Vector Machine (SVM), and one-to-one SVM method is used for the classification of EEG of six different movement patterns. Finally through the experimental verification, the validity of the proposed research method is demonstrated. The experiment has shown a better judging result, in which the average recognition rate is 78.9%.