Evaluation of the modified Stransform for timefrequency synchrony analysis and source localisation
 Said Assous^{1}Email author and
 Boualem Boashash^{2, 3}
https://doi.org/10.1186/16876180201249
© Assous and Boashash; licensee Springer. 2012
Received: 3 October 2011
Accepted: 28 February 2012
Published: 28 February 2012
Abstract
This article considers the problem of phase synchrony and coherence analysis using a modified version of the Stransform, referred to here as the Modified Stransform (MST). This is a novel and important timefrequency approach to study the phase coupling between two or more different spatially recorded entities with nonstationary characteristics. The basic method includes a crossspectral analysis to study the phase synchrony of nonstationary signals, and relies on some properties of the MST, such as phase preservation. We demonstrate the usefulness of the technique using simulated examples and real newborn EEG data. The results show the advantage of using the crossMST in the study of the connectivity between different signals using the timefrequency coherence. The MST led to improvements in resolution of almost twofold over the standard STransform in the examples presented in the article.
Keywords
1 Introduction
1.1 Timefrequency methods
Nonstationary signals have statistical properties that vary with time and hence the traditional time averaged amplitude spectrum obtained using Fourier transform is inadequate to track changes in signal magnitude, frequency or phase. In analyzing nonstationary and multicomponent signals, timefrequencybased techniques were shown to outperform classical techniques based on either time or frequency domains [1] (Chapter 1). The basic idea of timefrequency analysis is to understand and describe situations where the frequency content of a signal is changing in time. Although timefrequency analysis had its origin almost 50 years ago, significant advances have occurred in the past 20 years or so. In particular, the timefrequency representation has received considerable attention as a powerful high resolution and precision tool for analyzing a variety of biosignals and systems such as speech, ECG, EEG, PCG, EMG, as well as signals arising from other fields [2]. A timefrequency distribution (TFD) is used to analyze and process nonstationary signals in the joint timefrequency domain. Several TFDs exist in the literature [1]. Most of them are based on the WignerVille distribution (WVD) [3], as all the other TFDs can be expressed as a smoothed version of the WVD. A popular candidate of this class is the spectrogram, which is the square modulus of the short time Fourier transform (STFT). The spectrogram is the WVD smoothed in time and frequency by the ambiguity function of the window used in the STFT [4]; and all the quadratic TFDs (QTFD) are 2Dsmoothed versions of the WVD (and therefore of the spectrogram which is the square modulus of the STFT). The spectrogram has been widely used as an initial investigative tool [1] as it has the property of being a crossterms free TFD; but it also suffers from the undesirable tradeoff between time concentration and frequency concentration. To address the problem of crossterms suppression, while keeping a high timefrequency resolution, other TFDs have been proposed. Among these, one can cite the smoothed pseudo WVD (SPWVD) [1], the Cone shaped kernel TFD (ZAMD) [5], Gaussian TFD [6] and the Modified Bdistribution (MBD) [1] (Chapter 3) just to name a few. These methods form a class commonly referred to as QTFDs or Bilinear TFDs, as they represent and distribute the energy of a signal in the timefrequency domain. A different method, the continuous wavelet transform (CWT) has a wide area of application and can be seen as an extension of the spectrogram in a wide sense, except for representing signals in timescale space instead of timefrequency space. As it is linear and based on amplitude decomposition, it is also a useful technique for filtering in the timescale space. A more recently developed method called Stransform (ST) is conceptually a hybrid of the STFT and CWT. The ST uses a variable analyzing window length but preserves the phase information by using a Fourier kernel in the signal decomposition [7].
The above indicates that various methods of performing a timefrequency analysis exist. No method is superior to another, since they all have benefits for specific types of signals and applications to which they are adapted, depending on signal characteristics such as spectral contents.
The novel contributions in this article are mostly based on

The design of the modified ST (a signaldependent version of the standard ST with an improved timefrequency resolution).

The design and application of the crossmodified ST to investigate the phase synchrony between signals in the timefrequency plane and source location.
1.2 Phase synchrony and source location
Phase synchrony analysis is a useful measure of linear dependence between two stochastic signals. This approach is based on the concept of phase synchronization of chaotic oscillators studied by Rosenblum et al. [8]. The phase synchrony (coefficient) takes on values between 0, for two signals at different frequencies, and 1, for signals that exhibit a constant difference in instantaneous phase (representing the situation where a signal and its timeshifted version are observed). So, phase synchrony refers to the interdependence between the instantaneous phases of two signals; the instantaneous phases may be strongly synchronized even when the amplitudes of the two signals are statistically independent [8]. Estimating this measure requires prefiltering at a frequency of interest. The interesting properties of the phase synchrony coefficient are its independence of the signal amplitudes and that no assumptions about the nature of the signals are made. Another useful measure of linear dependence between two stochastic signals is the coherence function, which quantifies linear correlations in frequency domain based measure between two wide sense stationary signals [9, 10]. The magnitude squared coherence function "measures the degree to which one process can be represented as the output of a linear filter operating on the other process" [10] and varies from 0for two statistically independent processesto 1, when one process is the result of linear filtering performed on the other. The phase coherence is usually interpreted as a phase lead of one signal over the other; it finds many useful applications but the results based on coherence depend on several factors like stationarity of the signal, segment length, number of segments, etc. [11]. It is necessary to emphasize that although phase coherence and phase synchrony are quite similar and often are mixed up; they are two principally different measures. Phase coherence can be interpreted as phase shifts and amplitude changes over frequency between two correlated sequences, while phase synchrony indicates whether the phase shift is close to a constant over the specified time interval. This interpretation justifies narrowband filtering in the case of phase synchrony. The concept of a phase shift (either lead or lag) between two signals is only applicable when both signals are at the same frequency. One way to accomplish phase synchrony is via the Hilbert transform (HT); which consists of transforming the original signal, using an auxiliary function, into a complexvalued signal called an analytical form, from which the instantaneous phase is easily obtained [12]. This method relies on the assumption that the signal is composed of a narrow band of frequencies. Hence, it requires the bandpass filtering of the signal around a frequency of interest and then applies the HT to obtain the instantaneous phase. Another approach to estimate the phase synchrony is by performing a timevarying complex energy spectrum using either the CWT with a complex Morlet wavelet [13] or the shorttime Fourier transform (STFT) [14]. Being Gaussian and well localised in time and frequency domains, the Morlet wavelet has an optimal time and frequency resolution [13]. Basically, both CWT and STFT outperform the HTbased methods with the prior giving higher resolution phase synchrony estimates over time and frequency, especially at the low frequency range [8], as they take into account the nonstationarity of the signal. So, in contrast to HT, the waveletbased function has compact support in such way that it is defined only for those frequencies close to the frequency of interest, so it is equivalent to bandpass filtering at this frequency, which makes the prefiltering unnecessary. However, the phase information resulting from the wavelet transform is currently not fully understood, and is largely dependent on the specific wavelet, as it is essentially a timescale method and not a timefrequency method; it has the useful property of being linear in time for sinusoidal signals, and can thus be applied for phase synchrony. Although the wavelet and STFTbased phase synchrony approaches consider the nonstationarity issue, they suffer from a number of drawbacks. In the case of the wavelet transform, where we have a progressive timefrequency resolution the estimate won't be the same for the whole bandwidth of the signal, which means a non uniform timefrequency tiling on the analyzed signal and thus results in biased energy representations and corresponding phase estimates. In the case of STFT, there is a tradeoff between time and frequency resolution due to the window function. For these reasons, there is a need for a higher timefrequency resolution phase distributions that can track dynamic changes in phase synchrony over the whole timefrequency plane. As cited above, and given a measurements made from an array of electrodes or sensors, phase synchronization methods are used to detect the phase difference between two signals from two different electrodes or sensors, this can be applied for source location. First, appropriate neighborhood of each channel or electrode is constructed. Then phase synchronization is measured between the channel and each neighbor. Only those channels have been identified as closest to the potential sources where strong phase synchronization is detected will be considered for source location.
The article is organized as follow: The Section 2 introduces the ST definition, its key properties and provides a brief comparison between ST, CWT, and QTFD will be also reviewed. The Section 3 presents the concept and formulation of generalized ST and shows the improved resolution using the modified ST on synthetic examples. The Section 4 discusses the CrossMST for timefrequency coherence analysis on synthetic examples. The Section 5 demonstrates the application of the crossMST for phase synchrony detection to simulated EEG seizure and real newborn EEG data.
2 Advantages and limitations of the ST
The advent of timefrequency analysis techniques using QTFDs, STFT, and wavelet transforms made the analysis of nonstationary signals more precise. The low resolution of the STFT and the absence of phase information in the CWT led to the development of the ST, which has the property of retaining the absolute phase information, while preserving a good timefrequency resolution for all frequencies. Even though the ST has better timefrequency resolution compared to STFT, the resolution is far from perfect and needs improvement. The CWT uses a basis function which dilates and contracts with frequency; it does not retain the absolute phase information; further, the visual analysis of the timescale plots that are produced by the CWT is intricate. The ST combines the good features of STFT and CWT and can be viewed either as a variable sliding window STFT or as a phasecorrected CWT [7], as detailed below.
2.1 Definition and key relationships
The ST separates the mother waveletlike into two parts, the slowly varying envelope (the Gaussian function) which localises in time the amplitude modulated (AM) component, and the oscillatory exponential kernel e^{j 2πfτ}which selects the frequency being localised, the frequency modulated (FM) component. Hence, this separate AMFM decomposition is similar in concept to the empirical mode decomposition (EMD), which is a local and fully datadriven technique aimed at decomposing nonstationary multicomponent signals in "intrinsic" AMFM contributions [15]. The ST analyzing window is actually not a mother wavelet in a strict sense. It is the time localizing Gaussian parameter that is translated while the oscillatory exponential kernel remains stationary. By not translating the oscillatory exponential kernel in Equation (7), the ST localises the real and the imaginary components of the spectrum independently, therefore localizing the phase spectrum as well as the amplitude spectrum. This is referred to as absolutely referenced phase information [7]. Unlike the CWT, the ST produces a timefrequency representation instead of a timescale representation.
2.2 The ST and signal spectrum
This relationship shows that the concept of the ST is different from both the wavelet transform and QTFDs. The concept of recovering back the signal from the ST timefrequency plane is due to the fact that the phase is referenced at the origin, which means that the phase information given by the ST refers to the argument of the cosinusoid at zero time as it is the case for Fourier transform. In contrast, The phase spectrum of the signal can be extracted from its wavelet transform CWT, as a convolution of the signal with a complex wavelet, where the phase of the CWT is relative to the center (in time) of the analyzing wavelet (the mother wavelet). Thus as the wavelet translates, the reference point of the phase translates giving a locally referenced phase which is distinguishable from the phase properties of the ST and the signal can't be recovered from the timescale plane. On the other hand, the fact that QTFDs are bilinear and real valued energy distributions describing the energy of the signal over time and frequency, simultaneously, they cannot be used for estimating the phase of an individual signal and the phase synchrony between two signals. Moreover, the inverse process also is not straightforward, as it requires some initial conditions.
The equivalent discrete version of Equation (10) can be used to compute the discrete ST by taking advantage of the computational efficiency of the fast Fourier transform (FFT) and the convolution theorem.
2.3 Comparison of ST, CWT, and QTFDs
The similarities and differences between the ST, CWT, and QTFDs are reviewed below.
(1) ST and Morlet wavelet: Although the differences between the ST and Morlet wavelet are suitable, the only difference between ST and Morlet wavelet decomposition is that the ST timefrequency function is scaled by the carrier frequency f. Additionally, the amplitude modulation term f smoothes the ST spectrum, but may cause computational artifacts in the Morlet spectrum at low frequencies. Furthermore, the phase correction term in ST enable the ST to preserve absolute referenced phase information of a signal. Also, ST approach has advantages over the Morlet wavelet approach in terms of easy interpretation and fast computation.
(2) Sampling frequency: The discrete Fourier transform (DFT) has a very well defined sampling of the frequencies, in order to be both complete and orthonormal. The discrete ST has the identical sampling of the frequency space and retains the sampling of the signal in the time domain, Similar to a STFT this is redundant. On the other hand, CWT normally employs an octave scaling for frequencies, which results in an oversampled representation at the low frequencies and an under sampled representation at the higher frequencies. QTFDs have a sampling that is similar to the DFT.
(3) Invertibility and recoverable signal parameters: As the ST output is complex, we can read the amplitude A(t) = abs(S(t, f)), the frequency f, and the phase information $\varphi \left(t\right)=\mathsf{\text{atan}}\left(\frac{\mathsf{\text{Im}}\phantom{\rule{1em}{0ex}}\left(S\left(t,f\right)\right)}{\mathsf{\text{Re}}\phantom{\rule{1em}{0ex}}\left(S\left(t,f\right)\right)}\right)$ for each time step from the ST. This allows us to extract the corresponding signal by reproducing it in the time domain as follows: signal(t) = A(t)cos(2πf(t)t + ϕ(t)). The signal parameters were recoverable due to the combination of absolutely referenced phase information and frequency invariant amplitude of the ST, and such direct extraction cannot be done with CWTbased method. Moreover, the direct measurement of phase information makes the ST a potential candidate to estimate the phase synchrony between two signals.
For QTFDs, a signal x(t) can be recovered exactly, apart from a complex scaling factor [1, p. 61].
(4) ST phase: The ST retains the absolute phase information, where as the phase information is lost in the CWT. The absolutely referenced (at time t = 0) phase of the ST leads to a generalization of the instantaneous frequency to broadband signals and can be used as a local peakfinding algorithm [16, 17], for example. In contrast, in the wavelet approach; the phase is relative to the center (in time) of the analyzing wavelet, and as the wavelet translates, the reference point of the phase translates, hence the relative phase becomes meaningless. With the ST, the sinusoidal component of the basis function remains stationary, while the Gaussian envelope translates in time. Thus, the reference point for the phase remains stationary and the phase has the same meaning as in the Fourier domain [18]. For QTFDs, the phase information is linked to the time delay or instantaneous frequency [12]. The phase information can be extracted using the crossWVD or related methods [19].
(5) ST amplitude: The time domain localizing window (the Gaussian function) in Equation (7) is normalized by the factor $\frac{\leftf\right}{2\pi}$ to the unit area. In contrast to the CWT, this makes the amplitude response of the ST invariant to the frequency, which means that for a sinusoid with an amplitude A (x(t) = A cos(2π ft)), the ST returns an amplitude A regardless of the frequency f in a similar concept as the amplitude of the Fourier transform. On the other hand, the amplitude of the CWT is large for the lower frequencies and diminishes at the higher frequency components. This lower amplitude estimation at the high frequencies is mainly due to the normalization of the CWT [18]. For QTFDs, it is the signal energy that is distributed in t and f, not the signal amplitude. Hence, the interpretation is different. The spectrogram is the square modulus of the STFT; and as the QTFDS are a 2D convolution of the spectrogram, the spectrum X(f) can also be recovered subject to the initial complex constant X(f = 0).
(6) CrossST analysis: As the ST is complex and its phase characteristic is referenced at the origin, it can be employed in a cross spectrum analysis in a local manner to estimate the phase synchrony between two signals. Consider two signals measured by two receivers (transducers) separated by a known distance. Let a sinusoidal wave propagate through the medium of view of both receivers. What we will get is the same signal in both sensors with an additive noise (assuming a controlled environment conditions), but also with a time shift between the two signals. Since the ST is a linear operation on the signal and localizes spectral components in time, the cross correlation of specific events on two spatially separated STs will give the phase difference, hence the time delay between the two signals can be measured [18]. This concept is also found in crossTFDs using quadratic methods, where the signal and its complex conjugate are used to calculate the crossspectrum [19].
(7) CoST and quadratureST: As the local phase information can be extracted from the ST, we can use the crossST function to analyze the inphase and the outofphase components in timefrequency space. This is a very useful characteristic for crossspectral and phase synchrony. This property is exploited later for an array of EEG signals to estimate the phase synchrony between signals recorded from different electrodes and assess the source location of the brain activities. The crossST phase can be used to analyze this synchrony or asynchrony, thus providing a significant discriminating feature for potential abnormal brain activity. As with classical cospectrum analysis, the real part of the crossST function gives the inphase components of the local spectra. The imaginary part of the crossST function gives the inquadrature components. This property is discussed in detail in Section 3.
The above paragraphs indicate that, unlike the QTFDs, the ST is linear with progressive resolution (see Section 3.3.2 for more details). Unlike the CWT and STFT, the ST has a variable analysis window and an absolute phase reference. These useful properties of the ST make it a natural candidate to study the phase synchrony between two different spatially recorded signals.
2.4 Limitations of the ST
The ST was defined with two unnecessary restrictions on the window function detailed below.

Firstly, only a Gaussian window g(t, σ) is considered.

Secondly, frequency dependence of the analyzing window of the ST has been through horizontal and vertical dilations of the Gaussian window.

Also, that window has no parameters to allow its width in time or frequency to be adjusted, as shown in Equation (2).
The resolution, on the ST, of the onset times of events can be improved by using a narrower window, for example, using a better controlled parameters of the Gaussian window. However, when a window is narrowed in the time domain, it inevitably widens in the frequency domain, with consequent loss of resolution in the frequency direction on the ST compromising the identification of the whole event. One way of addressing this problem is to use extra parameters controlling the scale and the shape of the analyzing window rather than just only making the frequency inversely proportional to the standard deviation σ.
3 Extension of the ST for resolution improvement
3.1 Concept and definition
3.2 Key properties
where X(f) is the Fourier transform of the signal x(t).
3.3 Modified ST
3.3.1 Formulation of the modified ST
3.3.2 Improved resolution of the MST
In a similar concept to [20], The parameter $\frac{f}{\gamma}$ represents the number of cycles (periods) of a frequency that can be contained within one standard deviation σ of the Gaussian window given by Equation (2). Hence, we have a progressive improved resolution in this case. When too small, the Gaussian window retains very few cycles of the sinusoid and the frequency resolution degrades at low frequencies. In contrast, if it is too large, the window retains more cycles within it and in consequence, the time resolution degrades at high frequencies. This tradeoff between time and frequency resolution is governed by the "Heisenberg" uncertainty principle in a similar way as in the FT, ST or the CWT.
Figure 2b,c show clearly the difference in timefrequency resolution when the ST and the MST are applied. Using the MST leads to an improvement of almost twofold in resolution over the ST.
3.3.3 Simulated results, discussion and interpretation
In the previous example, the values of m and k were chosen depending on the length and the variance of the signal to get a better resolution (see Appendix 1 for more details). This indicates that as is the case with power spectral estimation, the approach is not restricted to a Gaussian window and any relevant window or apodising function may be employed to improve resolution. The method is not restricted to a Gaussian window and can be generalized in a similar concept to the CWT for which different windows can be used. Hence, the ST can be seen as a special case of the CWT using a Morlettype wavelet, with a phase and amplitude correction. This generalized form of ST is more flexible, as it uses two added parameters for varying the frequency in a linear way, resulting in adjustable windows for improved time or frequency resolution. Similar ideas led to the development of transforms based on groups generated by translations, modulations, and dilation of a mother wavelet [25]. All of these approaches give a more versatile choice of transform suitable to particular cases and applications. However, the particular characteristic of absolute phase, make the MST more attractive than wavelet when phase need to be estimated (e.g., in coherence analysis and crossspectral analysis). It can simultaneously estimate the local amplitude spectrum and the local phase spectrum, whereas a wavelet approach is only capable of probing the local amplitude/power spectrum. The MST fully estimates the amplitude of the signal, in contrast to the CWT which attenuates high frequencies. The MST can be further improved by dealing with issues such as fast algorithms, redundancy of representations, Hilbert space properties, resolution of the identity, among others [7].
4 CrossMST and phase synchrony
4.1 Motivation and illustration
where ()* denotes the complex conjugate.
Three key properties of the MST that make the crossMST useful are detailed below.
The above equation shows that the phase difference between two signals is equivalent to the crosspower spectrum. By Taking the inverse MST transform of the representation in the frequency domain, the displacement between two signals can be easily obtained (phase correlation technique).
The above derivation shows that we can use the cross spectral property to detect a time lag between the two signal as a function of t and f. A similar attempt was made in [26, 27] by using the crossWVD for representing nonstationary processes.
4.2 Illustration on simulated signals
5 Application to EEG seizure prediction
5.1 Phase synchrony information
The nonlinear interdependencies between fluctuations of brain physiological activities EEGbased recordings were intensively studied in pairs, and the synchrony flow differences were compared [28]. It was shown in [29] that a neurological disorder in the brain can be detected by the random occurrence of its clinical manifestations, i.e., the seizure. There is also evidence that the nonseizure (interictal) to seizure (ictal) transition is not an abrupt phenomenon [29]. Hence, to provide a valuable insight into such mechanism, identification of early changes in EEG signals [28] can be used for prediction, prevention, and control of upcoming seizures. Different approaches have been employed for seizure prediction in the past. Some limited techniques based on visual inspection of the EEG signals or on linear methods have failed to detect specific and sustained changes preceding seizures [28]. Other methods reported some changes based on the spectral contents [29], complexity [30], or spatiotemporal patterns of spikes [31]. Other recent approaches applied to newborn EEGbased seizure detection have been proposed such as timefrequency signal processing [32, 33], and nonlinear signal processing [34, 35].
Neuroscientists claim that when a focal seizure is generated, synchronized brain activity is initially observed only in a small area of the brain; and, from this focus, the activity spreads spatially out to other brain areas in the temporal lobe over time [29]. Involving the seizure focus spread into its surrounding, a hypersynchronous state happens and neighboring areas lose their synchronization with the other cortical regions around them [36]. In consequence, the seizure focus becomes isolated from the rest of the brain dynamics, making the considered population of neurons inactive. Methods to track these changes using coherence and synchronization are being explored using EEG, for the purpose of predicting an impending seizure [36]. Phase synchronization was also shown to be a sensitive indicator of coupling between signals and as an important factor in the genesis of epileptic phenomenon [36]. Many studies suggest an underlying correlation between neuronal synchronization and seizure development and onset [36, 37]. Thus, the concept of phase synchrony helped to measure the synchrony evolution while the amplitude of the signals remained uncorrelated. In contrast with crosscorrelation that measures linear relationships only, a phase coupling approach is able to show the presence of nonlinear coupling [38]. Moreover, phase synchrony can be used for nonperiodic and for chaotic signals such as EEG [8, 39]. Some authors used HT to measure phase synchrony between two signals [37, 38, 40]. However, the use of the HT assumes the signals have a narrow frequency band [1, p. 14], and it is not straightforward to extend the same analysis for broadband data. This assumption is usually seen to be ignored in the context of biomedical signals like the EEG [41]. As EEG signals are sometimes broadband (1100 Hz) [41], the HT may not be able to correctly estimate the instantaneous phase of broadband signals. This raises the concern that the broadband phase synchronization analysis may lead to a misinterpretation of the results [41]. Therefore, when the signal is broadband it is necessary to prefilter it in the frequency band of interest before applying the HT, in order to get an better estimate of the phase [42]. Some authors used waveletbased approach to analyze phase synchrony between pairs of EEG signals [13, 43–45]. These timevarying measures of phase synchrony using wavelet or HTs are similar in their results. Both give a sharper phase synchrony estimates over time and frequency, especially at the low frequency range [46]. Although the wavelet and Hilbert based phase synchrony estimates address the issue of nonstationarity of EEG signals, they suffer from limited timescale resolution due to the limited number of available scales in the case of wavelets and the narrowband assumption in the case of HT. Moreover, the role of phase in wavelet analysis is not as well understood as it is for the Fourier transform, especially for orthonormal wavelet representations. Current complex wavelets such as Daubechies, dual tree, and Shannon wavelets do not have a direct relationship to the signal spectrum [18]. For these reasons, this article proposes to use the MST and the crossMST to analyze the phase synchrony with the required resolution and with meaning, as the phase in the ST is meaningful and has the same concept as in the Fourier transform approach.
5.2 Coherence analysis using crossMST for simulated EEG seizure
Due to its nonstationarity characteristic, the newborn EEG seizure was modelled in the timefrequency domain [47–49]. using timefrequency characteristics previously identified in the newborn EEG seizure. Three models of newborn EEG seizure simulation were previously proposed. One model is based on some physiological parameters of the brain and utilises a stationary sawtooth waveform [50]. This approach is extended in [47] to incorporate a single linear frequency modulation (LFM) signal. Another piecewise LFM model was defined for seizure based on nonstationary inputs to a nonlinear model [51]. We propose here a method of newborn EEG seizure analysis using the piecewise LFM pattern outlined in [47–50] to simulate the nonstationary behavior of the newborn EEG seizures.
Key parts of the commented Matlab^{®} code written to implement this method are given in Appendix 2.
5.3 Coherence Analysis using crossMST for newborn EEG data
5.3.1 Background and analysis
5.3.2 Results, discussion and interpretation
The results on the real newborn EEG data show that the crossST can be used as a effective tool to study the phase coupling between channels. These results suggest that during the seizure period, all the components lose synchrony and are out of phase. This can be seen like a disorder in the neuronal activity during the seizure interval. This observed irregularity in the timefrequency patterns during the seizure period (4080 s) between two hemispheres in the posterior area suggest that different regions and not only one specific region may be causing the seizure. In contrast, the components (neural activity) became synchronized again after the seizure activity vanishes. This EEG seizure application demonstrates that the timefrequency coherence is an appropriate tool to study the phase coupling between different signals recorded from different spatially separated electrodes. This study is performed by the crossMST which has a referenced phase at the origin, a property that allows the study of phase coupling and timefrequency coherence between the neuronal activities. Although the crossMST represent a good candidate tool to analyze such coupling behavior, this methodology can be extended to other crosstimefrequency methods with high timefrequency resolution such as the QTFDs. In particular, the timefrequency coherence using the crossWVD has a useful property for cross spectral analysis, and can be interpreted as a time and frequency dependent correlation coefficients [26].
6 Conclusions and perspectives
This article applies the concept of timefrequency coherence using the crossMST which is a timevarying crossspectral analysis method obtained by extending the Stransform. The article demonstrates the ability of this timefrequency coherence using the crossMST to study the functional neuronal phase synchrony between channels. The performance of the phase synchrony estimation using the crossMST are evaluated both by simulated EEG seizure using a piecewise LFM signal to extract the in and outofphase components, and through the analysis of seizure EEG abnormalities in the newborn, where the dynamics of brain changes rapidly and the neurones lose their synchrony during seizure. Both the simulated and newborn results show crossMST phase and synchrony estimation to be more robust to noise. The resolution obtained by the MST shows almost twofold improvement over the standard ST approach. Future work will concentrate on a comparison study between the proposed method and other existing timefrequency techniques such as WignerVille and QTFDs based methods, for both simulated and real EEG data to develop more efficient timevarying phase and synchrony estimation for nonstationary signals, and such results will appear elsewhere. The proposed method can be extended to other physiological problems where the phase coupling is relevant, such in cardiorespiratory relationship, for example, where the coupling can be affected by cardiovascular diseases.
Acknowledgements
The Authors wish to thank Prof Paul Colditz, Director of the Perinatal Research Centre, and his team for providing the real newborn EEG data presented in this paper. The second author acknowledges funding by QNRF, Qatar Foundation under grant NPRP 09 465 2 174. The first author also acknowledges Weatherford for giving permission to publish this work.
Appendix
1 Computing the MST
% Compute the MST
[M,N]=size(sig); % get the length of the signal
N2=fix(N/2); j = 1;
if N2*2==N; j = 0;end
f=[0:N2 N2+1j:1]/N; %frequency
MST=zeros(N2+1,N); %allocate memory for positive frequencies of MST.
SIG=fft(sig,N); %compute the signal spectrum
g=(1/N)*f+4*var(sig); %parameter gamma
for i = 2:N2
SIGs=circshift(SIG,[0,(i1)]); %circshift the spectrum SIG
W=(g(i)/f(i))*2*pi*f; % Scale Gaussian
G=exp((W.^2)/2); %W in Fourier domain
MST(i,:)=ifft(SIGs.*G); %Compute the complex values of MST
end
imagesc(abs(MST)');
set(gca,'Ydir','Normal'); %default Y axis in imagesc is inverted.
2 Generation of piecewise LFM
%Piecewise LFM chirp generation for EEG Seizure simulation
fs = 50; %Sampling frequency
t1=(5:1/50:5); t2=(2.5:1/50:2.5); t3=(5:1/50:5); % time for the first,
% the second and the
% third segment
t=[t1 t2 t3]; % total time for the Piecewise LFM.
f00 = 2.626; f01 = 1.83; f10 = 1.751; % start frequencies
a1=0.7; a2=0.6; a3 = 0.2; % chirp rates or slope
ph1 = 2*pi*f00*t1+0.5*a1*t1.*t1; % quadratic phase of piecewise LFM1
ph2 = 2*pi*f01*t2+0.5*a2*t2.*t2; % quadratic phase of piecewise LFM2
ph3 = 2*pi*f10*t3+0.5*a3*t3.*t3; % quadratic phase of piecewise LFM3
lfm1=exp(1i*ph1);lfm2=exp(1i*ph2);lfm3=exp(1i*ph3); %generate piecewise LFMs
lfm=[real(lfm1) real(lfm2) imag(lfm3)]; % put all in one signal LFM.
sig=lfm+0.2*randn(length(lfm),1)'; % add gaussian noise
3 Computing the crossMST
%The crossMST, coMST and quadratureMST
%Computing the CrossMST of two signal x(t) and y(t)
%having as modified ST: MST1 and MST2, respectively.
% 1.Compute MST1 and MST2 using the related code in appendix A.
% 2.Compute the crossMST between MST1 and MST2
cross MST = MST1.*conj(MST2); %Get the crossMST.
% 3. Get the outofphase components
imagesc(imag(cross_MST)); set(gca,'Ydir','Normal')
% 4. Get the inphase components
imagesc(real(cross_MST)); set(gca,'Ydir','Normal')
Declarations
Authors’ Affiliations
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