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.