Adaptive Spatial Filtering for Brain Event-Related Potentials Analysis

In this work we describe a novel data driven spatial filtering technique that can be applied to the evoked potentials in the EEG data in order to find statistically significant hidden differential activations, which can not be found by standard single-channel analysis. Underlying optimization problem is formulated as a generalized Rayleigh quotient maximization problem. The technique is based on the known morphological characteristics of the response: the optimal filter maximizes the difference in the target interval when the component usually occurs and at the same time minimizes the difference in the flanker interval. The technique equipped with a relevant randomization-based statistical test to assess the significance of thus discovered phenomenon. The performance of the proposed method was evaluated with the simulated ERP data, the results are compared with the competing ICA-based method. Furthermore, we describe an application of the proposed method to the EEG data acquired in two studies: study devoted to the simultaneous language interpreting (group analysis) and analysis of the auditory neuroplasticity (single subject application). We show how the differential components can be detected after filtration and support our results with the permutation statistical test, topographies analysis and single-trial evidences.