Multivariate evoked response detection based on the spectral F-test

Abstract

BackgroundObjective response detection techniques, such as magnitude square coherence, component synchrony measure, and the spectral F-test, have been used to automate the detection of evoked responses. The performance of these detectors depends on both the signal-to-noise ratio (SNR) and the length of the electroencephalogram (EEG) signal. New methodRecently, multivariate detectors were developed to increase the detection rate even in the case of a low signal-to-noise ratio or of short data records originated from EEG signals. In this context, an extension to the multivariate case of the spectral F-test detector is proposed. ResultsThe performance of this technique is assessed using Monte Carlo. As an example, EEG data from 12 subjects during photic stimulation is used to demonstrate the usefulness of the proposed detector. Comparison with existing method(s)The multivariate method showed detection rates consistently higher than those ones when only one signal was used. ConclusionsIt is shown that the response detection in EEG signals with the multivariate technique was statistically significant if two or more EEG derivations were used.