Multivariate approach for estimating the local spectral F-test and its application to the EEG during photic stimulation.

BACKGROUND AND OBJECTIVE: The local spectral F-test (SFT) corresponds to a statistical way of assessing whether the spectrum of a signal is flat in the vicinity of a specific frequency. The power of this univariate test (comparing one frequency component  against its neighbours using only one signal) depends on the signal-to-noise ratio, which is fixed in the case of electroencephalogram (EEG) analysis. However, this limitation could be overcome by considering more signals in the analysis. Thus, this work presents an alternative multivariate approach for estimating the local SFT. METHODS: Probabilities of detection and false alarm studies were performed for this new detector using Monte Carlo simulations and theoretically whenever possible. The application was illustrated in recorded EEG data collected during photic stimulation. RESULTS: The results showed that it is worth using more channels if available, since the probability of detecting a response tends to increase with increasing number of signals. In the application to the EEG during photic stimulation, the best results were obtained by using N > 2 signals (around 30% more accurate when compared with the univariate case. The false positive levels were maintained below 5%). CONCLUSION: Consequently, it is conjectured that it is always better to apply the proposed method if more than one EEG signal with the same signal-to-noise ratio (SNR) is available. For the case where the SNRs are different, a guideline has been given to improve the detection.

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

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.

Multivariate objective response detectors (MORD): statistical tools for multichannel EEG analysis during rhythmic stimulation.

The presence of cerebral evoked responses can be tested by using objective response detectors. They are statistical tests that provide a threshold above which responses can be assumed to have occurred. The detection power depends on the signal-to-noise ratio (SNR) of the response and the amount of data available. However, the correlation within the background noise could also affect the power of such detectors. For a fixed SNR, the detection can only be improved at the expense of using a longer stretch of signal. This can constitute a limitation, for instance, in monitored surgeries. Alternatively, multivariate objective response detection (MORD) could be used. This work applies two MORD techniques (multiple coherence and multiple component synchrony measure) to EEG data collected during intermittent photic stimulation. They were evaluated throughout Monte Carlo simulations, which also allowed verifying that correlation in the background reduces the detection rate. Considering the N EEG derivations as close as possible to the primary visual cortex, if N = 4, 6 or 8, multiple coherence leads to a statistically significant higher detection rate in comparison with multiple component synchrony measure. With the former, the best performance was obtained with six signals (O1, O2, T5, T6, P3 and P4).