A matrix-based algorithm for estimating multiple coherence of a periodic signal and its application to the multichannel EEG during sensory stimulation

The coherence between the stimulation signal and the electroencephalogram (EEG) has been used in the detection of evoked responses. The detector's performance, however, depends on both the signal-to-noise ratio (SNR) of the responses and the number of data segments (M) used in coherence estimat...

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Vydané v:IEEE transactions on biomedical engineering Ročník 51; číslo 7; s. 1140 - 1146
Hlavní autori: Miranda de Sa, A.M.F.L., Felix, L.B., Infantosi, A.F.C.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.07.2004
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adolescent
Algorithms
Biomedical engineering
Biomedical measurements
Brain - physiology
Child
Detectors
Diagnosis, Computer-Assisted - methods
Electroencephalography
Electroencephalography - methods
Electronic mail
Evoked Potentials, Visual - physiology
Humans
Monitoring
Pediatrics
Periodicity
Regression Analysis
Reproducibility of Results
Sampling methods
Sensitivity and Specificity
Signal to noise ratio
Studies
ISSN:0018-9294, 1558-2531
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Popis
Shrnutí:The coherence between the stimulation signal and the electroencephalogram (EEG) has been used in the detection of evoked responses. The detector's performance, however, depends on both the signal-to-noise ratio (SNR) of the responses and the number of data segments (M) used in coherence estimation. In practical situations, when a given SNR occurs, detection can only be improved by increasing M and hence the total data length. This is particularly relevant when monitoring is the objective. In the present study, we propose a matrix-based algorithm for estimating the multiple coherence of the stimulation signal taking into account a set of N EEG channels as a way of increasing the detection rate for a fixed value of M. Monte Carlo simulations suggest that thresholds for such multivariate detector are the same as those for multiple coherence of Gaussian signals and that using more than six signals is not advisable for improving the detection rate with M=10. The results with EEG from 12 normal subjects during photic stimulation at 10 Hz showed a maximum detection for N greater than 2 in 58% of the subjects with M=10, and hence suggest that the proposed multivariate detector is valuable in evoked responses applications.
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ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2004.827952