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K J Blinowska

Publications and source records attributed to K J Blinowska.

27 records · Page 2Linked to original sources
PubMed1985

The application of parametric multichannel spectral estimates in the study of electrical brain activity.

A parametric autoregressive model was applied to the multichannel EEG time series. Small statistical fluctuations of the spectral estimates obtained from the short data strings made possible to follow the time changes of the signals. The multiple and partial coherences were calculated for the four channel process and compared with the coherences computed between the pairs of channels. From the study it followed that the partial coherences are the proper measure of the synchronization of brain structures and their intrinsic relationships. The partial phase spectra give the information about the phase delays. The advantages of the parametric description of signals in the frequency domain in respect to the modelling of dynamic systems was pointed out.

Brain↗
PubMed1985

Linear model of brain electrical activity--EEG as a superposition of damped oscillatory modes.

EEG time series were modeled as an output of the linear filter driven by white noise. Parameters describing the signal were determined in a way fulfilling the maximum entropy principle. Transfer function and the impulse response function were found. The solutions of the differential equations describing the system have the form of the damped oscillatory modes. The representation of the EEG time series as a superposition of the resonant modes with characteristic decay factors seems a valuable method of the analysis of the signal, since it offers high reduction of the data to the few parameters of a clear physiological meaning.

Animals↗
PubMed1981

EEG data reduction by means of autoregressive representation and discriminant analysis procedures.

A program for automatic evaluation of EEG spectra, providing considerable reduction of data, was devised. Artefacts were eliminated in two steps: first, the longer duration eye movement artefacts were removed by a fast and simple 'moving integral' methods, then occasional spikes were identified by means of a detection function defined in the formalism of the autoregressive (AR) model. The evaluation of power spectra was performed by means of an FFT and autoregressive representation, which made possible the comparison of both methods. The spectra obtained by means of the AR model had much smaller statistical fluctuations and better resolution, enabling us to follow the time changes of the EEG pattern. Another advantage of the autoregressive approach was the parametric description of the signal. This last property appeared to be essential in distinguishing the changes in the EEG pattern. In a drug study the application of the coefficients of the AR model as input parameters in the discriminant analysis, instead of arbitrary chosen frequency bands, brought a significant improvement in distinguishing the effects of the medication. The favourable properties of the AR model are connected with the fact that the above approach fulfils the maximum entropy principle. This means that the method describes in a maximally consistent way the available information and is free from additional assumptions, which is not the case for the FFT estimate.

Brain↗
PubMedDate not supplied

Analysis of EEG transients by means of matching pursuit.

Matching pursuit (MP), a new technique of time-frequency signal analysis, was applied to simulated signals and the awake and sleep EEG. With the MP algorithm, waveforms from a very large class of functions were fitted to the local signal structures in a recursive procedure. By means of this technique, sleep spindles were localized in the time-frequency plane with high precision, and their intensities and time spans were found. The MP technique makes following the temporal evolution of transients and their propagation in brains possible. It opens up new possibilities in EEG research providing a means of investigation of dynamic processes in brains in a much finer time-frequency scale than any other method available at present.

Algorithms↗
PubMedDate not supplied

The methods of automatic analysis of epileptic EEG.

The methods of automatic evaluation of epileptic EEG are reviewed. The aims of the computer analysis of seizure activity and different approaches to this problem are presented.

Electroencephalography↗