Improving the detection of evoked responses to periodic stimulation by using multiple coherence--application to EEG during photic stimulation.
The coherence between the stimulation signal and the electroencephalogram (EEG) has been used in the detection of evoked responses. However the detector's performance depends on both the signal-to-noise ratio (SNR) of the responses and the number of data segments (M) used in coherence estimation. In this work, a technique for detecting evoked responses was developed based on the extension to the multivariate case of this coherence. Thus, instead of using the EEG collected at a unique region, the estimation is proposed using two EEG derivations. As for the univariate case, this multiple coherence is independent of the stimulation signal. In addition, considering equal SNR in both signals, the detection rate with this multiple coherence is always greater than that one using only one signal. This was verified in Monte Carlo simulations, which also showed that a superior performance is still expected in practical situations, when a smaller SNR is found in the second signal. The results with EEG from 12 normal subjects during photic stimulation confirm this better performance. Since the proposed technique allows a higher detection rate without the need of increasing M, it permits evoked responses to be detected faster, which is very useful during monitored surgeries.
Miranda de Sá
Antonio Mauricio F L
AM
Departamento de Eletricidade (DEPEL), Fundação de Ensino Superior de São João del Rei-FUNREI, São João del Rei, MG, Brazil. amiranda@funrei.br
Felix
Leonardo B
LB
D000293:Adolescent; D002648:Child; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D010507:Periodicity; D010775:Photic Stimulation / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D013269:Stochastic Processes