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.
Keywords: Electroencephalogram; Local spectral F-test; Objective response detection.
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