A Novel Algorithm for Learning Sparse Spatio-Spectral Patterns for Event-Related Potentials

Recent years have witnessed brain-computer interface (BCI) as a promising technology for integrating human intelligence and machine intelligence. Currently, event-related potential (ERP)-based BCI is an important branch of noninvasive electroencephalogram (EEG)-based BCIs. Extracting ERPs from a limited number of trials remains challenging due to their low signal-to-noise ratio (SNR) and low spatial resolution caused by volume conduction. In this paper, we propose a probabilistic model for trial-by-trial concatenated EEG, in which the concatenated ERPs are expressed as a linear combination of a set of discrete sine and cosine bases. The bases are simply determined by the data length of a single trial. A sparse prior on the rank of the spatio-spectral pattern matrix is introduced into the model to allow the number of components to be automatically determined. A maximum posterior estimation algorithm based on cyclic descent is then developed to estimate the spatiospectral patterns. A spatial filter can then be obtained by maximizing the SNR of the ERP components. Experiments on both synthetic data and real N170 ERP from 13 subjects were conducted to test the efficacy and efficiency of the algorithm. The results showed that the proposed algorithm can estimate the ERPs more accurately than the several state-of-the-art algorithms.