An offline/real-time artifact rejection strategy to improve the classification of multi-channel evoked potentials

Authors:
Hyunseok Kook;Lalit Gupta;Srinivas Kota;Dennis Molfese;H. Lyytinen
Affiliations:
Developmental Neuropsychology Laboratory at the Birth Defects Center, University of Louisville, Louisville, KY 40292, USA;Department of Electrical & Computer Engineering, Southern Illinois University, Carbondale, IL 62901, USA;Department of Electrical & Computer Engineering, Southern Illinois University, Carbondale, IL 62901, USA;Developmental Neuropsychology Laboratory at the Birth Defects Center, University of Louisville, Louisville, KY 40292, USA;Department of Psychology, University of Jyvaskyla, Finland
Venue:
Pattern Recognition
Year:
2008

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Abstract

The primary goal of this paper is to improve the classification of multi-channel evoked potentials (EPs) by introducing a temporal domain artifact detection strategy and using this strategy to (a) evaluate how the performance of classifiers is affected by artifacts and (b) show how the performance can be improved by detecting and rejecting artifacts in offline and real-time classification experiments. Using a pattern recognition approach, an artifact is defined in this study as any signal that may lead to inaccurate classifier parameter estimation and inaccurate testing. The temporal domain artifact detection tests include: a within-channel standard deviation (STD) test that can detect signals with little or abnormal variations in each channel and also detect faulty channels, a within-channel clipping (CL) test to detect amplitude clipped EPs in each channel, and a multi-channel EP median distance (MC-MED) test to detect atypical signals not identified by the STD and CL tests. Because the MC-MED test is class-dependent, a novel ''pre-testing'' approach is developed to identify artifacts in real-time classification experiments. The performance of the artifact detection strategy is demonstrated on real single-trial EP ensembles and it is shown that the strategy is quite effective in identifying atypical EPs. In order to demonstrate the effects of artifacts on classifier performance, a series of classification experiments are designed using a multi-channel decision fusion classification algorithm. Specifically, the classification performance is evaluated on (a) real EP ensembles with artifact contaminations in the training and test sets and (b) ensembles that are free of artifacts in both the training and test sets. It is shown that the improvement in classification accuracy through the incorporation of the artifact detection strategy can be quite significant in real-time classification trials. Furthermore, the generalized formulation of the artifact rejection classification strategy makes it adaptable to various other problems involving the multi-class classification of multivariate signals of multiple sensors.