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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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Classical quantification of ERD/ERS in three bands: α 10–12 Hz, (β 14–18 Hz, γ 36–40 Hz. Insert indicates positions of electrodes used for recording. C3, Cz and C4 from the 10–20 system are marked by thicker circles. Results presented for electrode marked by filled circle [22].
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Figure 23: Classical quantification of ERD/ERS in three bands: α 10–12 Hz, (β 14–18 Hz, γ 36–40 Hz. Insert indicates positions of electrodes used for recording. C3, Cz and C4 from the 10–20 system are marked by thicker circles. Results presented for electrode marked by filled circle [22].

Mentions: Advantages of the MP algorithm with stochastic dictionaries can be also combined with the stochastic element present inherently in the data, like e.g. in the case of analyzing repetitions of event-related potentials. This relates especially to the non-phase locked activity, i.e. such that would not be enhanced in the stimulus-synchronized time averages. Its detection requires a different analysis technique, allowing for averaging signal's energy irrelevant of the phase2. Classically it was achieved by squaring the values of signals, band-pass filtered in a priori chosen frequency bands, before averaging ([14], Figure 23).


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

Durka PJ - Biomed Eng Online (2003)

Classical quantification of ERD/ERS in three bands: α 10–12 Hz, (β 14–18 Hz, γ 36–40 Hz. Insert indicates positions of electrodes used for recording. C3, Cz and C4 from the 10–20 system are marked by thicker circles. Results presented for electrode marked by filled circle [22].
© Copyright Policy
Related In: Results  -  Collection

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

Figure 23: Classical quantification of ERD/ERS in three bands: α 10–12 Hz, (β 14–18 Hz, γ 36–40 Hz. Insert indicates positions of electrodes used for recording. C3, Cz and C4 from the 10–20 system are marked by thicker circles. Results presented for electrode marked by filled circle [22].
Mentions: Advantages of the MP algorithm with stochastic dictionaries can be also combined with the stochastic element present inherently in the data, like e.g. in the case of analyzing repetitions of event-related potentials. This relates especially to the non-phase locked activity, i.e. such that would not be enhanced in the stimulus-synchronized time averages. Its detection requires a different analysis technique, allowing for averaging signal's energy irrelevant of the phase2. Classically it was achieved by squaring the values of signals, band-pass filtered in a priori chosen frequency bands, before averaging ([14], Figure 23).

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