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From wavelets to adaptive approximations: time-frequency parametrization of EEG.

Durka PJ - Biomed Eng Online (2003)

Bottom Line: This paper presents a summary of time-frequency analysis of the electrical activity of the brain (EEG).It covers in details two major steps: introduction of wavelets and adaptive approximations.This conclusion is followed by a brief discussion of the current state of the mathematical and algorithmical aspects of adaptive time-frequency approximations of signals.

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Affiliation: Laboratory of Medical Physics, Institute of Experimental Physics, Warsaw University, Warszawa, Poland. piotr@durka.info

ABSTRACT
This paper presents a summary of time-frequency 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 time-frequency solutions to several standard research and clinical problems, encountered in analysis of evoked potentials, sleep EEG, epileptic activities, ERD/ERS and pharmaco-EEG. 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 time-frequency approximations of signals.

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Single AEP, reconstructed as in Figure 2
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Figure 3: Single AEP, reconstructed as in Figure 2

Mentions: First successful applications of wavelets addressed the issue of evoked potentials (EP). Event-related potentials are small changes of EEG activity in response to an external (evoked) or internal stimulus. They are usually an order of magnitude weaker than the on-going EEG activity, so traditionally they were observed only in time averages of stimulus-synchronized repetitions. Owing to the property of the time synchronization, they can be studied in the space of orthogonal wavelet coefficients. Figures 2,3,4 present the first wavelet study of evoked potentials [5]. Randomly chosen epochs of on-going EEG were statistically compared – in the space of wavelet coefficients – to those synchronized by an auditory stimulus. As a result, only 9 out of 512 coefficients were found to discriminate between those conditions. They were enough to reproduce the basic morphology of the average EP. Figure 4 presents reconstruction of single evoked potentials using those coefficients.


From wavelets to adaptive approximations: time-frequency parametrization of EEG.

Durka PJ - Biomed Eng Online (2003)

Single AEP, reconstructed as in Figure 2
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC149437&req=5

Figure 3: Single AEP, reconstructed as in Figure 2
Mentions: First successful applications of wavelets addressed the issue of evoked potentials (EP). Event-related potentials are small changes of EEG activity in response to an external (evoked) or internal stimulus. They are usually an order of magnitude weaker than the on-going EEG activity, so traditionally they were observed only in time averages of stimulus-synchronized repetitions. Owing to the property of the time synchronization, they can be studied in the space of orthogonal wavelet coefficients. Figures 2,3,4 present the first wavelet study of evoked potentials [5]. Randomly chosen epochs of on-going EEG were statistically compared – in the space of wavelet coefficients – to those synchronized by an auditory stimulus. As a result, only 9 out of 512 coefficients were found to discriminate between those conditions. They were enough to reproduce the basic morphology of the average EP. Figure 4 presents reconstruction of single evoked potentials using those coefficients.

Bottom Line: This paper presents a summary of time-frequency analysis of the electrical activity of the brain (EEG).It covers in details two major steps: introduction of wavelets and adaptive approximations.This conclusion is followed by a brief discussion of the current state of the mathematical and algorithmical aspects of adaptive time-frequency approximations of signals.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Medical Physics, Institute of Experimental Physics, Warsaw University, Warszawa, Poland. piotr@durka.info

ABSTRACT
This paper presents a summary of time-frequency 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 time-frequency solutions to several standard research and clinical problems, encountered in analysis of evoked potentials, sleep EEG, epileptic activities, ERD/ERS and pharmaco-EEG. 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 time-frequency approximations of signals.

Show MeSH
Related in: MedlinePlus