Improving the detection of evoked responses to periodic stimulation by using bivariate local spectral F-test -...

The spectral local F-test has been applied for detecting evoked responses to rhythmic stimulation that are embedded in the ongoing electroencephalogram (EEG). Based on the sampling distribution of a flat spectrum at the neighbourhood of the stimulation frequency, spectral peaks in an EEG signal that are due to the stimulation may be readily assessed. Nevertheless, the performance of the technique is strongly affected by both the signal-to-noise ratio (SNR) of the responses and the number of data segments used in the estimation. The present work aims at both deriving and evaluating a multivariate extension of local F-test by including the EEG collected at a second distinct derivation. The detection rate with this multivariate detector was found to be greater than that using a single channel in case of equal SNR in both signals. Monte Carlo simulation results showed that the probability of detection with this new detector saturates for signal-to-noise ratios above 12 dB and indicated a greater detection rate in practical situations, even when smaller SNR-values are found in the added signal (e.g. 5 dB for 16 neighbouring frequencies used in the estimation). The technique was next applied to the EEG from 12 subjects during intermittent, photic stimulation leading to superior performance in comparison with the univariate local F-test. Since a higher detection rate with the proposed technique is achieved without the need of increasing the number of data segments, it allows evoked responses to be detected faster, once the same detection rate may be accomplished with less segments. This might be useful in clinical practice.