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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.

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.

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Complete time-frequency map (from 5 to 40 Hz), from which slices presented in Figure 23 were cut. Horizontal scale-seconds relative to the finger movement, vertical-frequency (Hz). Top-the same in 3 dimensions, but energy of the alpha band cut off in 50% of the height to show weaker high-frequency structures [22].
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Figure 25: Complete time-frequency map (from 5 to 40 Hz), from which slices presented in Figure 23 were cut. Horizontal scale-seconds relative to the finger movement, vertical-frequency (Hz). Top-the same in 3 dimensions, but energy of the alpha band cut off in 50% of the height to show weaker high-frequency structures [22].

Mentions: This methodology has several drawbacks, including tha arbitrary choice of frequency bands and limited sensitivity (c.f. Figure 20). Averaging the time-frequency densities of energy, obtained from the MP decomposition, allows for an instantaneous evaluation of the complete picture of changes in the time-frequency plane, with high resolution. Figure 25 presents this picture for the same data as in Figure 23. Another study in Figure 26 reveals clearly splitting of the alpha band in the upper and lower frequencies, with different behaviour related to the finger movement. This phenomenon would be very difficult to find using the classical methodology.


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

Durka PJ - Biomed Eng Online (2003)

Complete time-frequency map (from 5 to 40 Hz), from which slices presented in Figure 23 were cut. Horizontal scale-seconds relative to the finger movement, vertical-frequency (Hz). Top-the same in 3 dimensions, but energy of the alpha band cut off in 50% of the height to show weaker high-frequency structures [22].
© Copyright Policy
Related In: Results  -  Collection

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

Figure 25: Complete time-frequency map (from 5 to 40 Hz), from which slices presented in Figure 23 were cut. Horizontal scale-seconds relative to the finger movement, vertical-frequency (Hz). Top-the same in 3 dimensions, but energy of the alpha band cut off in 50% of the height to show weaker high-frequency structures [22].
Mentions: This methodology has several drawbacks, including tha arbitrary choice of frequency bands and limited sensitivity (c.f. Figure 20). Averaging the time-frequency densities of energy, obtained from the MP decomposition, allows for an instantaneous evaluation of the complete picture of changes in the time-frequency plane, with high resolution. Figure 25 presents this picture for the same data as in Figure 23. Another study in Figure 26 reveals clearly splitting of the alpha band in the upper and lower frequencies, with different behaviour related to the finger movement. This phenomenon would be very difficult to find using the classical methodology.

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