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.

Abstract-The coherence function has been widely applied in quantifying the degree of synchronism between electroencephalogram (EEG) signals obtained from different brain regions. However, when applied to investigating synchronization resulting from rhythmic stimulation, misleading results can arise from the high correlation of background EEG activity. We, thus propose a modified measure, which emphasizes the synchronized stimulus responses and reduces the influence of the spontaneous EEG activity. Critical values for this estimator are derived and tested in Monte Carlo simulations. The effectiveness of the method is illustrated on data recorded from 12 young normal subjects during rhythmic photic stimulation.

The aim of this work was to reduce ECG artifacts from surface electromyogram (EMG) signals collected from lumbar muscles with the blind source separation technique based on independent component analysis (ICA). Using four EMG signals collected above erector spinal lumbar muscles from 27 subjects, the proposed method fail in separating the sources. However, when considering a single channel of EMG and the same one time-shifted by one sample, the FastICA allowed reducing the signal to ECG noise ratio.

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The sampling distribution of the multiple coherence estimate between a periodic signal and a set of filtered versions of evoked responses embedded in additive noise signals is derived for the zero-coherence case. For a fixed number of signals used in the estimation, the probability density function varies with the number of data segments. Analytical expressions for both bias and variance of the estimate were derived and together with the critical values constitute the statistical apparatus for the detector based on this multiple coherence estimate. An illustration of the technique as applied to detect evoked responses in the Electroencephalogram during sensory stimulation is also provided.