Using eigenstructure decompositions of time-varying autoregressions in common spatial patterns-based EEG signal classification

Brain–computer interfaces based on common spatial patterns (CSP) depend on the operational frequency bands of the events to be discriminated. This problem has been addressed through sub-band decompositions of the electroencephalographic signals using filter banks, then the performance relies on the number of filters that are stacked and the criteria to select their bandwidths. Here, we propose an alternative approach based on an eigenstructure decomposition of the signals’ time-varying autoregressions (TVAR). The eigen-based decomposition of the TVAR allows for subject-specific estimation of the principal time-varying frequencies, then such principal eigencomponents can be used in the traditional CSP-based classification. We show through a series of numerical experiments that the proposed classification scheme can achieve a performance which is comparable with the one obtained through the filter bank-based approach. However, our method does not rely on a preliminary selection of a frequency band, yet good performance is achieved under realistic conditions (such as reduced number of sensors and small amount of training data) independently of the time interval selected.

► We propose a method where an eigenstructure decomposition of EEG signals’ time-varying autoregressions (TVAR) are used to select the EEG's most relevant frequency components.
► This process allows for an automatic selection of the operational frequency band in a common spatial patterns-based classification process for brain–computer interface (BCI) applications.
► We evaluate the performance of the proposed method under realistic conditions, such as reduced number of measuring channels, reduced training and short time intervals. The evaluation is based on the ROC curves of a Mahalanobis distance-based classifier.
► The proposed method was tested using real EEG data corresponding to previous BCI international competitions.
► Our results show that the proposed classification scheme can achieve good performance under such realistic conditions without affecting the computation of the spatial patterns, then it is a good candidate for real-life applications.