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Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (8): 687-697    DOI: 10.1631/jzus.C10b0359
    
Incremental expectation maximization principal component analysis for missing value imputation for coevolving EEG data
Sun Hee Kim, Hyung Jeong Yang*, Kam Swee Ng
Department of Computer Science, Chonnam National University, Gwangju 500-757, Korea
Incremental expectation maximization principal component analysis for missing value imputation for coevolving EEG data
Sun Hee Kim, Hyung Jeong Yang*, Kam Swee Ng
Department of Computer Science, Chonnam National University, Gwangju 500-757, Korea
 全文: PDF(503 KB)  
摘要： Missing values occur in bio-signal processing for various reasons, including technical problems or biological characteristics. These missing values are then either simply excluded or substituted with estimated values for further processing. When the missing signal values are estimated for electroencephalography (EEG) signals, an example where electrical signals arrive quickly and successively, rapid processing of high-speed data is required for immediate decision making. In this study, we propose an incremental expectation maximization principal component analysis (iEMPCA) method that automatically estimates missing values from multivariable EEG time series data without requiring a whole and complete data set. The proposed method solves the problem of a biased model, which inevitably results from simply removing incomplete data rather than estimating them, and thus reduces the loss of information by incorporating missing values in real time. By using an incremental approach, the proposed method also minimizes memory usage and processing time of continuously arriving data. Experimental results show that the proposed method assigns more accurate missing values than previous methods.
关键词： Electroencephalography (EEG)')" href="#">Electroencephalography (EEG);  Missing value imputation;  Hidden pattern discovery;  Expectation maximization;  Principal component analysis    
Abstract: Missing values occur in bio-signal processing for various reasons, including technical problems or biological characteristics. These missing values are then either simply excluded or substituted with estimated values for further processing. When the missing signal values are estimated for electroencephalography (EEG) signals, an example where electrical signals arrive quickly and successively, rapid processing of high-speed data is required for immediate decision making. In this study, we propose an incremental expectation maximization principal component analysis (iEMPCA) method that automatically estimates missing values from multivariable EEG time series data without requiring a whole and complete data set. The proposed method solves the problem of a biased model, which inevitably results from simply removing incomplete data rather than estimating them, and thus reduces the loss of information by incorporating missing values in real time. By using an incremental approach, the proposed method also minimizes memory usage and processing time of continuously arriving data. Experimental results show that the proposed method assigns more accurate missing values than previous methods.
Key words: Electroencephalography (EEG)    Missing value imputation    Hidden pattern discovery    Expectation maximization    Principal component analysis
收稿日期: 2010-10-14 出版日期: 2011-08-03
CLC:  TP391  
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Sun Hee Kim
Hyung Jeong Yang
Kam Swee Ng

引用本文:

Sun Hee Kim, Hyung Jeong Yang, Kam Swee Ng. Incremental expectation maximization principal component analysis for missing value imputation for coevolving EEG data. Front. Inform. Technol. Electron. Eng., 2011, 12(8): 687-697.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C10b0359    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I8/687

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