Coherence between a pulse train representing periodic stimuli and the EEG has been used in the objective detection of steady-state evoked potentials. This work aimed to quantify the strength of the stimulus responses based on the statistics of coherence estimate between one random and one periodic signal, focusing on the confidence limits and power of significance tests in detecting responses. To detect the responses in 95% of cases, a signal-to-noise ratio of about -7.9 dB was required when using 48 windows (M) in the coherence estimation. The ratio, however, increased to -1.2 dB when M was 12. The results were tested in Monte Carlo simulations and applied to EEGs obtained from 14 subjects during visual stimulation. The method showed differences in the strength of responses at the stimulus frequency and its harmonics, as well as variations between individuals and over cortical regions. In contrast to those from the parietal and temporal regions, results for the occipital region gave confidence limits (with M = 12) that were above zero for all subjects, indicating statistically significant responses. The proposed technique extends the usefulness of coherence as a measure of stimulus responses and allows statistical analysis that could also be applied usefully in a range of other biological signals.
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).
Partial coherence estimate between two signals removing the contribution of a periodic, deterministic one is proposed for measuring the coherence between two ongoing eletroencephalografic (EEG) activities collected at distinct cortical regions under sensory stimulation. The estimator expression was derived and shown to be independent of the stimulating signal. Simulations were used for obtaining the critical values for this coherence estimate. The technique was also evaluated throughout simulations and next applied to the EEG from 12 subjects under intermittent photic stimulation at 4 and 6 Hz. In both simulation and EEG data, major differences between partial and simple coherences occurred at the stimulation frequency and harmonics, except for those falling within the alpha band. These findings suggest that the technique is highly selective in removing the contribution of the periodic source. They also indicate high coherence values of the ongoing EEG within the alpha band.
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