Few-trial Extraction of Evoked Potentials with Dictionary Training and Morphological Component Analysis

The few-trial extraction of evoked potentials is very meaningful to the study of brain and many clinical applications. In this paper, we proposed a few-trial extraction method based on the morphological component analysis. That is, the evoked potential and the electroencephalogram were sparsely represented in the different overcomplete dictionaries. To avoid the error representation due to the selection of inappropriate dictionaries, we used the average result of several noisy signals as the template signal and employed the K-SVD algorithm to obtain the appropriate overcomplete dictionaries in accordance with different signals, and then sparsely represented the corresponding signals in these trained dictionaries. Experimental results show that the algorithm can reduce the inappropriate representation efficiently versus the method with the universal overcomplete dictionaries, and it can extract the evoked potentials better than the latter.