Optimal Pairing of Signal Components Separated by Blind Techniques

In this letter, the problem of optimal pairing of signal components separated by blind techniques in different time-windows or in different frequency bins is addressed. The optimum pairing is defined as the one which minimizes the sum of some distances (criteria of dissimilarity) of the to-be-assigned signal components. It is shown that the optimal pairing can be achieved by the Kuhn-Munkres algorithm known in graph theory as a solution to the optimal assignment problem. An advantage of the proposed pairing method is shown on data from electroencephalogram, which are blindly separated using the FastICA algorithm in a sliding time-window with the aim to study the time evolution of elements of the estimated mixing matrix.