Classification of Time-Embedded EEG Using Short-Time Principal Component Analysis

Principal component analysis (PCA) is often used to project high-dimensional signals to lower dimensional subspaces defined by basis vectors that maximize the variance of the projected signals. The projected values can be used as features for classification problems. Data containing variations of relatively short duration and small magnitude, such as those seen in EEG signals, may not be captured by PCA when applied to time series of long duration. Instead, PCA can be applied independently to short segments of data and the basis vectors themselves can be used as features for classification. Here this is called the short-time principal component analysis (STPCA). In addition, the time-embedding of EEG samples is investigated prior to STPCA, resulting in a representation that captures EEG variations in space and time. The resulting features of the analysis are then classified via a standard linear discriminant analysis (LDA). Results are shown for two datasets of EEG, one recorded from subjects performing five mental tasks, and one from the third BCI Competition recorded from subjects performing one mental task and two imagined movement tasks.