From wavelets to adaptive approximations: timefrequency parametrization of EEG
 Piotr J Durka^{1}Email author
https://doi.org/10.1186/1475925X21
© Durka; licensee BioMed Central Ltd. 2003
Received: 21 September 2002
Accepted: 6 January 2003
Published: 6 January 2003
Abstract
This paper presents a summary of timefrequency analysis of the electrical activity of the brain (EEG). It covers in details two major steps: introduction of wavelets and adaptive approximations. Presented studies include timefrequency solutions to several standard research and clinical problems, encountered in analysis of evoked potentials, sleep EEG, epileptic activities, ERD/ERS and pharmacoEEG. Based upon these results we conclude that the matching pursuit algorithm provides a unified parametrization of EEG, applicable in a variety of experimental and clinical setups. This conclusion is followed by a brief discussion of the current state of the mathematical and algorithmical aspects of adaptive timefrequency approximations of signals.
Keywords
EEG
The electroencephalogram (EEG), recorded from electrodes placed on the scalp, reflects the electrical activity of the brain.
First registered from human scalp in 1929 [1], until today EEG remains an important tool in neuroscience and clinical neurophysiology. For a long time it was the only objective parameter providing information on brain's function. Recently emerging dynamic imaging techniques like fMRI and PET offer a complementary information on brains functioning. Their drawbacks include significantly lower time resolution, high cost and invasiveness. Nevertheless, in spite of those drawbacks, they are often preferred for the straightforward interpretability. On the contrary, visual interpretation of EEG is a difficult, tedious and complicated task, requiring many years of experience. And even then it contains a significant subjective factor:
Every experienced electroencephalographer has his or her personal approach to EEG interpretation. (...) there is an element of science and element of art in a good EEG interpretation; it is the latter that defies standardization.
writes Prof. Ernst Niedermayer in the recent edition of a fundamental reference [2].
 1.
visual analysis of raw EEG traces,
 2.
Fourier estimation of spectral power in selected frequency bands,
 3.
description of evoked potentials, averaged in the time domain.
One of the reasons of this unsatisfactory situation is that among the variety of new methods, proposed each year for the analysis of EEG, very few have any direct relationship to the traditional, visual analysis. That means that their results usually cannot be directly related to the most valuable knowledge base of 70 years of experience, collected by means of the visual analysis of EEG. On the contrary, each new method needs to be evaluated from the scratch, in many laboratories and over many repeated studies, before it can be considered as a clinically usable parameter. And since there is a multitude of new methods being proposed, with few a priori hints for their possible performance, this process seems to be hardly convergent.
Wavelets: the first step towards timefrequency analysis
EEG as a signal is extremely complex and reveals both rhythmical and transient features. Therefore, when orthogonal wavelets triggered in eighties the timefrequency revolution in signal processing, analysis of EEG was one of the fields where hopes for a more "morphological" description of signal arose.
Limitations of linear and quadratic timefrequency representations
Both these problems can be solved (or at least significantly reduced) by the solution proposed in the next section.
Adaptive approximations and matching pursuit
Given a set of functions (dictionary) D = {g _{1},g _{2}...g _{ n }} such that g _{ i } = 1, we can define an optimal Mapproximation as an expansion, minimizing the error ε of an approximation of signal f(t) by M waveforms:
where {γ_{ i }}_{ i = 1..M } represents the indices of the chosen functions g _{γi}. Finding such an optimal approximation is an NPhard problem. A suboptimal expansion can be found by means of an iterative procedure, such as the matching pursuit algorithm (MP) proposed by Mallat and Zhang [7].
In the first step of MP, the waveform g _{γ0} which best matches the signal f(t) is chosen. In each of the consecutive steps, the waveform g _{γn } is matched to the signal R^{ n } f, which is the residual left after subtracting results of previous iterations:
Orthogonality of R^{ n+1} f and g _{γn } in each step implies energy conservation:
For a complete dictionary the procedure converges to f:
From this equation we can derive a timefrequency distribution of the signal's energy, that is free of crossterms, by adding Wigner distributions of selected functions:
This magnitude is presented in Figure 7, calculated from MP decomposition of a simulated signal with known and simple content. We observe that most of the structures are represented compactly and with high resolution, except for the structure of changing frequency (linear chirp). It is represented by a series of structures of constant frequency, since in the applied Gabor dictionary (section 5) we have only constant frequency modulations. Section 7 presents an alternative approach to this issue.
Figure 8 presents estimates of the timefrequency density of the same signal's energy, obtained from: spectrograms with different window widths, continuous wavelet transform and smoothed pseudo WignerVille distribution. Only in the last case representation of the chirp looks better than on the plot obtained from MP decomposition, but we must take into account that in this case the parameters of the kernel of the distribution were optimized for this particular signal.
Except for the lack of cross terms and high resolution, adaptive timefrequency parametrizations exhibit one more basic and important advantage over the continuous timefrequency representations. Unlike the maps from Figure 8, for all the structures presented in Figure 7 we have a priori the exact values of their time and frequency centers, widths, amplitudes and phases. This property will be thoroughly explored in the following studies.
First application in EEG analysis: sleep spindles

says the definition from the basic reference [8] – "A manual of standardized terminology, techniques and scoring system for sleep stages in human subjects". It can be directly translated into the language of parameters of the structures fitted to the signal by the algorithm discussed in the previous section.
Results presented in these figures conform also the previously observed effect of predominance of lower frequencies of sleep spindles in frontal derivations and higher frequencies in parietal derivations. We also notice that some frequencies on these plots are "prefereed", i.e. exhibit regular maxima in the histograms. But are they preferred by the brain or by the analysis algorithm? This question will be resolved in the next section.
Discrete Gabor dictionary
Gabor functions (sinemodulated Gaussian functions) provide optimal joint timefrequency localization. A real Gabor function can be expressed as:
N is the size of the signal for which the dictionary is constructed, K(γ) is such that g _{γ} = 1. γ = {u,ω,s,φ } denotes parameters of the dictionary's functions (timefrequency atoms). These parameters generate a threedimensional continuous space; this results in an infinite dictionary size. Therefore, in practical implementations, we use subsets of the possible dictionary's functions.
In the dictionary implemented originally by Mallat and Zhang in [7], parameters of these functions (timefrequency atoms) are chosen from dyadic sequences of integers. Their sampling is governed by an extra parameter – octave j (integer). Scale s, which corresponds to an atom's width in time, is derived from the dyadic sequence s = 2^{ j }, 0 ≤ j ≤ L (signal size N = 2^{ L }). Parameters u and ω, which correspond to position in time and frequency, are sampled for each octave j with interval s = 2^{ j }, or, if oversampling by l is introduced, with interval 2^{ jl }.
Stochastic dictionaries
In forming the dictionary used for MP decomposition, using any fixed scheme of subsampling the parameter space introduces statistical bias in the resulting parametrization. This bias becomes apparent only when statistics comes into play, like in parameterization of large amounts of data. Unbiased MP decompositions can be obtained by an analogue of Monte Carlo methods.
In [12] we proposed MP with stochastic dictionaries, where the parameters of a dictionary's atoms are randomized before each decomposition. A stochastic dictionary D is constructed for a given signal length N and chosen "resolutions"^{1} in time, frequency and scale (Δt, Δω and Δs). The space of parameters t, ω and s is thereby divided into bricks of size Δt by Δω by Δs each, where ω ∈ (0,π). In each of those bricks, one timefrequency atom is chosen by drawing its parameters from flat distributions within the given ranges of continuous parameters.
^{1}The resolution of the matching pursuit is hard to define in general, since the procedure is nonlinear and signaldependent. It should be related to the distance between neighboring dictionary waveforms available for decomposition. In the described procedure, this distance does not exceed twice the value of the corresponding parameter (Δt, Δω or Δs).
Pharmaco EEG
This section presents an application of the proposed methodology to the standard clinical problem of testing influence of sleep inducing drugs on the sleep EEG.
The effects of zolpidem, midazolam and placebo, administered at bed time, were studied in 7 paid volunteers. From each of the 21 allnight recordings (3 nights for each subject) we extracted artifactfree epochs from derivation C3A2 of the 10–20 system. For the analysis of slow wave activity (SWA) we used data from stages III and IV and for sleep spindles only data from sleep stage II.
Criteria chosen for the automatic selection of EEG structures based upon the timefrequency parameters.
frequency  duration  amplitude  

SWA  0.75–4 Hz  0.5∞s.  >75 μV 
sleep spindles  12–14 Hz  0.5–2.5 s.  > 15 μV 
 1.
In the recording of patient ZCB09, which revealed a very low spindling activity (last row in Figures 16 and 17), fluctuations in the background masked the power of sleep spindles to such an extent, that spectral integrals indicate a partially reverse trend, i.e. decrease of power of sleep spindles under the influence of midazolam. Selective estimates indicate the expected increase.
 2.
For patient ZCB08 (second row from the bottom) spectral integrals indicate increase of power of SWA (δ), while MP estimates reveal a decrease coherent with all the other cases.
Finally, as an example of an effect elusive to the classical methodology, these results indicate also a statistically significant decrease of the frequency of the slow wave activity. Further discussion and statistical evaluation of results can be found in [13].
Representation of changing frequency
However, by averaging several decompositions of the same signal in different realizations of smaller stochastic dictionaries, we obtain a more continuous representation like in Figures 22 (g) and Figure 21 (c). The representation is no more compact, but retains the general property of absence of cross terms.
Eventrelated desynchronization and synchronization
^{2}If we want to analyze phasic synchronization in higher frequencies, like e.g. gamma band around 40 Hz, the accuracy of the triger's determination would have to be much better than e.g. 1 ms, which is rather beyond the accuracy of determining e.g. the moment of a finger movement. But nonphasic effects should be visible in the presented approach.
Epileptic seizures
Discussed advantages of timefrequency estimates of energy density, obtained from MP decomposition, allowed for identification of different dynamic states during evolution and propagation of epileptic seizures in [15].
Problems
Other, more theoretical examples of MP failures are given in [16] and [17]. Some of these cases can be properly solved by the orthogonalized matching pursuit [18], at a cost of increased computational requirements and a possibility of introducing numerical instabilities [19]. Another modification of the MP algorithm, discussed in [20], relies on a modification of the similarity function used in each step to choosed the "best fit". Other works [21] indicate that global optimalization of the l^{1} norm of expansion's coefficients might be the best choice [17], but, in spite of recent advances in linear programming, computational complexity is still very high.
Other problems, awaiting better solutions, include: efficient and (at the same time) biasfree implementations, robust estimation of resolution, proper addressing of the tradeoff between resolution (increasing dictionary size) and computation cost, and extension of the algorithm to the multichannel case. Such research will improve effectivity and understanding of mathematical foundations of these higly nonlinear procedures. However, current implementations and knowledge of algorithm's properties are already sufficient for large scale applications in EEG research and clinical practice. They may serve as a basis for a unification of parametrization and description of EEG.
Implementations of MP with stochastic dictionaries is available from http://brain.fuw.edu.pl/mp.
Authors contributions
Parts of presented research, according to the quoted references, were coauthored by:

Prof. Katarzyna J. Blinowska, head of the Laboratory of Medical Physics, Warsaw University, and my colleagues from the Lab: Józef Ginter Jr, Dobiesław Ircha, Jarosław Zygierewicz.

Prof. Waldemar Szelenberger, Medical University of Warsaw

Prof Ernest A. Bartnik, Inst. of Theoretical Physics, Warsaw University

Prof. Piotr Franaszczuk, Johns Hopkins Medical University

Prof. Gert Pfurtscheller and dr Christa Neuper, Graz Technical University
Declarations
Authors’ Affiliations
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