A Matrix-Based Algorithm for Estimating Multiple Coherence of a Periodic Signal and Its Application to the Multichannel EEG During Sensory Stimulation

de, A.M.F.L. Miranda; Félix, Leonardo Bonato; Infantosi, Antonio Fernando Catelli

doi:10.1109/tbme.2004.827952

Cited by 42 publications

(23 citation statements)

References 6 publications

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“…Hitherto, thresholds of univariate coherence of ϳ0.4 -0.5 have been used by most investigators to accept spectral estimates at selected frequency bands (4,7,10,15,19,35,40). Different methods have been used to estimate confidence limits for univariate and multiple coherence functions (23). In many cases, Monte Carlo simulations have been performed by using Gaussian noise to obtain the distribution of coherence under the null hypothesis.…”

Section: Discussionmentioning

confidence: 99%

Multiple coherence of cerebral blood flow velocity in humans

Panerai¹,

Eames²,

Potter³

2006

American Journal of Physiology-Heart and Circulatory Physiology

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The coherence function has been used in transfer function analysis of dynamic cerebral autoregulation to assess the statistical significance of spectral estimates of gain and phase frequency response. Interpretation of the coherence function and choice of confidence limits has not taken into account the intrinsic nonlinearity represented by changes in cerebrovascular resistance due to vasomotor activity. For small spontaneous changes in arterial blood pressure (ABP), the relationship between ABP and cerebral blood flow velocity (CBFV) can be linearized, showing that corresponding changes in cerebrovascular resistance should be included as a second input variable. In this case, the standard univariate coherence function needs to be replaced by the multiple coherence, which takes into account the contribution of both inputs to explain CBFV variability. With the use of two different indicators of cerebrovascular resistance index [CVRI ϭ ABP/CBFV and the resistance-area product (RAP)], multiple coherences were calculated for 42 healthy control subjects, aged 20 to 40 yr (28 Ϯ 4.6 yr, mean Ϯ SD), at rest in the supine position. CBFV was measured in both middle cerebral arteries, and ABP was recorded noninvasively by finger photoplethysmography. Results for the ABP ϩ RAP inputs show that the multiple coherence of CBFV for frequencies Ͻ0.05 Hz is significantly higher than the corresponding values obtained for univariate coherence (P Ͻ 10 Ϫ5 ). Corresponding results for the ABP ϩ CVRI inputs confirm the principle of multiple coherence but are less useful due to the interdependence between CVRI, ABP, and CBFV. The main conclusion is that values of univariate coherence between ABP and CBFV should not be used to reject spectral estimates of gain and phase, derived from small fluctuations in ABP, because the true explained power of CBFV in healthy subjects is much higher than what has been usually predicted by the univariate coherence functions. cerebral autoregulation; coherence function; transfer function analysis; multivariate modeling; transcranial Doppler ultrasound TRANSFER FUNCTION ANALYSIS (TFA) is a useful technique to study physiological systems whose properties are characterized by linear or quasi-linear dynamic relationships between two or more physiological time series (6, 36). One area that has benefited from this approach is the study of dynamic cerebral blood flow (CBF) autoregulation in humans (27,28,40). Aaslid et al. (2) have extended the classical concept of "static" autoregulation (20) by showing that a sudden change in arterial blood pressure (ABP) leads to a relatively fast recovery of CBF to its original level. This transient response of the autoregulatory mechanisms is what is now termed "dynamic" cerebral autoregulation (CA) (2). TFA of dynamic CA was initially proposed by Giller (13), who modeled CA as an input-output relationship between beat-to-beat values of ABP and CBF, as estimated by transcranial Doppler ultrasound (TCD) recordings of CBF velocity (CBFV). The many contributions that followed...

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Section: Discussionmentioning

confidence: 99%

Multiple coherence of cerebral blood flow velocity in humans

Panerai¹,

Eames²,

Potter³

2006

American Journal of Physiology-Heart and Circulatory Physiology

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“…The critical values of this multiple coherence between the stimulation signal and a set of EEG signals have been recently achieved through Monte Carlo simulation. 12 According to that investigation they are the same as those for multiple coherence of Gaussian signals and are given by…”

Section: Multiple Magnitude-squared Coherence Of a Periodicalmentioning

confidence: 99%

“…where Y ji ( f ) is the ith-window Fourier Transform of y j [k] in the harmonics of the stimulation frequency, and N ji ( f ) is the associate noise term transform of n j [k], whose real and imaginary parts are zero mean, uncorrelated with each other, white Gaussian noises with variance r f 2 [see 12 for more details on the simulation]. The number N of signals was continuously incremented to iteratively find the minimum SNR in the next added signal that would lead to improvement in the detection rate (significance level a = 0.05) in comparison with that using the previous N ) 1 signals.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

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Multivariate Objective Response Detectors (MORD): Statistical Tools for Multichannel EEG Analysis During Rhythmic Stimulation

et al. 2006

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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).

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“…The multiple coherence estimate between a stimulus x[k] and a set of N signals y[k] is given by [21] The critical value of MC for a significance level , M EEG epochs and N derivations, can be calculated as:…”

Section: Multiple Coherence (Mc)mentioning

confidence: 99%

Objective detection of Steady State Visual Evoked Responses for different lighting conditions

Tannús

Tierra-Criollo

Melges

2013

J. Phys.: Conf. Ser.

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Abstract:This work aims at evaluating the performance of two Objective Response Detection (ORD) techniques: the Magnitude-Squared Coherence (MSC) and the Multiple Coherence (MC) for detecting Steady State Visual Evoked Response (SSVEP) for two different lighting conditions (scotopic and photopic). EEG signals were collected (using a wireless headset) from ten volunteers without history of neurological diseases during stimulation with white LED flickering at frequencies of 6, 7, 8, 9, 10 and 11 Hz. The experimental protocol was composed by 13 sessions in a dark room and 13 in a bright one. The frequency order was randomized. The MSC was applied to derivations [P7], [P8], [O1] and [O2], and MC to the combinations of leads [O1-O2], [O1-O2-P7], [O1-O2-P8] and [O1-O2-P7-P8]. Both estimates were calculated using M = 11 epochs. The detection rates for MSC presented an important decrease at photopic condition compared to the scotopic one for all derivations and all frequencies. The employment of MC to [O1-O2-P7-P8] allowed overcoming this limitation, leading to detection percentages of at least 80% for all frequencies (except 9 Hz). These results show that MC is a promising technique for building SSVEP Bran Computer Interfaces (BCI).

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A Matrix-Based Algorithm for Estimating Multiple Coherence of a Periodic Signal and Its Application to the Multichannel EEG During Sensory Stimulation

Cited by 42 publications

References 6 publications

Multiple coherence of cerebral blood flow velocity in humans

Multiple coherence of cerebral blood flow velocity in humans

Multivariate Objective Response Detectors (MORD): Statistical Tools for Multichannel EEG Analysis During Rhythmic Stimulation

Objective detection of Steady State Visual Evoked Responses for different lighting conditions

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