{"title":"基于CSP的脑电心理任务时频功率特征提取与f -评分优化","authors":"Xinjie Wang, Lin Ma, Haifeng Li, M. Wu","doi":"10.1109/IMCCC.2015.179","DOIUrl":null,"url":null,"abstract":"Aiming the mental tasks recognition tasks in brain computer interface (BCI), this paper proposes an Electroencephalography (EEG) feature extraction method which makes use of the discriminative common spatial patterns (CSP). Apart from CSP analysis's traditional application in time domain, it is extended to consider frequency domain information of EEG signal. After an artifact removal through the independent component analysis (ICA), the multichannel EEG signals are decomposed into a set of spatial patterns by CSP analysis, and the logarithmic frequency domain and time domain power distributions are calculated. Time-frequency power features are extracted on these distributions and optimized by a F-score method. Comparing with the traditional CSP methods, the proposed method not only retained the time domain variance features, but also induced the frequency band power features. Since F-score is easy and fast to calculate, and the F-score based method can quickly select more effective features from high dimensional data, depending on the importance and the discriminative ability of each data pattern. In our method, the F-score algorithm is also used to solve the traditional CSP problems such as the definition of common pattern number. The proposed method was tested on a five-task cognitive state analysis problem, and a recognition accuracy of 89.4% was achieved, that well approved the effectiveness and versatility of the proposed method.","PeriodicalId":438549,"journal":{"name":"2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CSP Based Extraction and F-Score Based Optimization of Time-Frequency Power Features for EEG Mental Task Classification\",\"authors\":\"Xinjie Wang, Lin Ma, Haifeng Li, M. Wu\",\"doi\":\"10.1109/IMCCC.2015.179\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming the mental tasks recognition tasks in brain computer interface (BCI), this paper proposes an Electroencephalography (EEG) feature extraction method which makes use of the discriminative common spatial patterns (CSP). Apart from CSP analysis's traditional application in time domain, it is extended to consider frequency domain information of EEG signal. After an artifact removal through the independent component analysis (ICA), the multichannel EEG signals are decomposed into a set of spatial patterns by CSP analysis, and the logarithmic frequency domain and time domain power distributions are calculated. Time-frequency power features are extracted on these distributions and optimized by a F-score method. Comparing with the traditional CSP methods, the proposed method not only retained the time domain variance features, but also induced the frequency band power features. Since F-score is easy and fast to calculate, and the F-score based method can quickly select more effective features from high dimensional data, depending on the importance and the discriminative ability of each data pattern. In our method, the F-score algorithm is also used to solve the traditional CSP problems such as the definition of common pattern number. The proposed method was tested on a five-task cognitive state analysis problem, and a recognition accuracy of 89.4% was achieved, that well approved the effectiveness and versatility of the proposed method.\",\"PeriodicalId\":438549,\"journal\":{\"name\":\"2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IMCCC.2015.179\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMCCC.2015.179","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
CSP Based Extraction and F-Score Based Optimization of Time-Frequency Power Features for EEG Mental Task Classification
Aiming the mental tasks recognition tasks in brain computer interface (BCI), this paper proposes an Electroencephalography (EEG) feature extraction method which makes use of the discriminative common spatial patterns (CSP). Apart from CSP analysis's traditional application in time domain, it is extended to consider frequency domain information of EEG signal. After an artifact removal through the independent component analysis (ICA), the multichannel EEG signals are decomposed into a set of spatial patterns by CSP analysis, and the logarithmic frequency domain and time domain power distributions are calculated. Time-frequency power features are extracted on these distributions and optimized by a F-score method. Comparing with the traditional CSP methods, the proposed method not only retained the time domain variance features, but also induced the frequency band power features. Since F-score is easy and fast to calculate, and the F-score based method can quickly select more effective features from high dimensional data, depending on the importance and the discriminative ability of each data pattern. In our method, the F-score algorithm is also used to solve the traditional CSP problems such as the definition of common pattern number. The proposed method was tested on a five-task cognitive state analysis problem, and a recognition accuracy of 89.4% was achieved, that well approved the effectiveness and versatility of the proposed method.