Wenxiao Zhong, X. An, Yang Di, Lixin Zhang, Dong Ming
{"title":"基于脑电信号的隐马尔可夫模型识别","authors":"Wenxiao Zhong, X. An, Yang Di, Lixin Zhang, Dong Ming","doi":"10.1145/3444884.3444889","DOIUrl":null,"url":null,"abstract":"The researches on individual identification approaches based on EEG signals draw lots of attention in recent years. Few of them got time-robust identification performance. In this study, we focused on the time robustness of individual identification using EEG under conditions of resting-state of eye open/closed (REO/REC). Ten subjects participated in this study and each of them conducted three independent runs experiment, with the time intervals between adjacent runs were at least two weeks. There were three sessions within each run, and the time duration of each session is 150 seconds of REO/REC. Two features, auto-regressive (AR) and Mel-frequency cepstrum coefficients (MFCC) were calculated as identity features. Then Support vector machines (SVM) and Hidden Markov model (HMM) were used as classifiers. To access the time-robust performance of our methods, we used one of three runs data as test set and the other two as training set. Results show that the best classification accuracy is 80%. It is believed that under the conditions of REC and REO, the identity features of most subjects are robust across time and can be used for identification. This study will have an important impact on in EEG-based identification system.","PeriodicalId":142206,"journal":{"name":"Proceedings of the 2020 7th International Conference on Biomedical and Bioinformatics Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Using Hidden Markov Model for Identification Based on EEG Signals\",\"authors\":\"Wenxiao Zhong, X. An, Yang Di, Lixin Zhang, Dong Ming\",\"doi\":\"10.1145/3444884.3444889\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The researches on individual identification approaches based on EEG signals draw lots of attention in recent years. Few of them got time-robust identification performance. In this study, we focused on the time robustness of individual identification using EEG under conditions of resting-state of eye open/closed (REO/REC). Ten subjects participated in this study and each of them conducted three independent runs experiment, with the time intervals between adjacent runs were at least two weeks. There were three sessions within each run, and the time duration of each session is 150 seconds of REO/REC. Two features, auto-regressive (AR) and Mel-frequency cepstrum coefficients (MFCC) were calculated as identity features. Then Support vector machines (SVM) and Hidden Markov model (HMM) were used as classifiers. To access the time-robust performance of our methods, we used one of three runs data as test set and the other two as training set. Results show that the best classification accuracy is 80%. It is believed that under the conditions of REC and REO, the identity features of most subjects are robust across time and can be used for identification. This study will have an important impact on in EEG-based identification system.\",\"PeriodicalId\":142206,\"journal\":{\"name\":\"Proceedings of the 2020 7th International Conference on Biomedical and Bioinformatics Engineering\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2020 7th International Conference on Biomedical and Bioinformatics Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3444884.3444889\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 7th International Conference on Biomedical and Bioinformatics Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3444884.3444889","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using Hidden Markov Model for Identification Based on EEG Signals
The researches on individual identification approaches based on EEG signals draw lots of attention in recent years. Few of them got time-robust identification performance. In this study, we focused on the time robustness of individual identification using EEG under conditions of resting-state of eye open/closed (REO/REC). Ten subjects participated in this study and each of them conducted three independent runs experiment, with the time intervals between adjacent runs were at least two weeks. There were three sessions within each run, and the time duration of each session is 150 seconds of REO/REC. Two features, auto-regressive (AR) and Mel-frequency cepstrum coefficients (MFCC) were calculated as identity features. Then Support vector machines (SVM) and Hidden Markov model (HMM) were used as classifiers. To access the time-robust performance of our methods, we used one of three runs data as test set and the other two as training set. Results show that the best classification accuracy is 80%. It is believed that under the conditions of REC and REO, the identity features of most subjects are robust across time and can be used for identification. This study will have an important impact on in EEG-based identification system.