PubMed Health⌕ Search

Biomedical subjects

K J Blinowska

Publications and source records attributed to K J Blinowska.

At least 19 recordsLinked to original sources
PubMed1998

Time-frequency analysis of vibrotactile driving responses by matching pursuit.

A new method of time-frequency analysis, based on the Matching Pursuit (MP) algorithm, was used to extract and quantify EEG 'driving' or frequency-following responses produced in human primary somatosensory cortex (SI) by 33 Hz vibrotactile stimulation of the right index fingertip in a single subject. EEG signals were recorded from a 5 x 5 array of electrodes centered over the left hand area, time-locked to repeated presentations of four vibratory stimulus amplitudes. The MP algorithm was used to decompose the edited and and filtered EEG signals into waveforms selected from a large and redundant dictionary. Statistical discrimination of the vibratory stimulus amplitudes was then readily achieved in terms of trial-by-trial measures of response amplitude constructed in automated fashion from the calculated MP parameters. The results were orderly and physiologically coherent, and potentially open the way to correlation of psychophysical magnitude estimates with measures of neurophysiological response on a trial-by-trial basis. The approach developed here appears well suited to detection and characterisation of time dependent or transient target signals embedded in a noisy background.

Algorithms↗
PubMed1997

Information flow between hippocampus and related structures during various types of rat's behavior.

The relationships among the CA1 field of hippocampus, the entorhinal-piriform area, the subiculum and the lateral septum were studied in various behavioral states in the rat. The EEG signals recorded simultaneously from chronically implanted electrodes were analyzed by means of a multichannel autoregressive (AR) model. Power spectra, ordinary, multiple and partial coherences, and directed transfer functions were calculated. The method of analysis which took into account all signals simultaneously, not pair-wise, made it possible to estimate the spectral characteristics and the directions of the EEG flow between structures. The pattern of the EEG activity propagation depended on the type of behavior, difficulty of the task performed by the animal, and the phase of the trial. Our results not only confirmed the existence of connections between analyzed structures, but also showed that these connections may have different strengths during various types of behavior.

Animals↗
PubMed1997

Introduction to wavelet analysis.

Wavelet transform and multiresolution decomposition are described. Examples of the application of orthogonal wavelet transform to acoustic evoked potentials and otoacoustic emissions (OEA) are given and basic features of wavelet packets and wavelet network methods are characterized. An approach that enables the identification of local signal structures--a generalization of wavelet transform called Matching Pursuit--is presented. In the framework of this method the signal is decomposed into time-frequency 'atoms', which offers a possibility of determination of an 'instantaneous frequency' with the accuracy close to the theoretical limit. The method is illustrated by application to OAE signals. The advantages and limitations of the methods presented are discussed.

Cochlea↗
PubMed1997

High resolution time-frequency analysis of otoacoustic emissions.

High resolution time-frequency analysis of OAE signals evoked by stimuli of different strength was performed by means of the Matching Pursuit algorithm. The method relies on adaptive decomposition of a signal into waveforms of well-defined frequency and time localization. Energy of OAE as a function of time and frequency was evaluated for stimuli strength of 35-80 dB SPL. Dynamic characteristics of the signal were constructed. For strong stimuli decrease of the power of high frequency components was found. Matching Pursuit proved to be a method which offers high resolution parametrisation of OAE in time-frequency space and provides excellent possibilities of investigation of the signal generation mechanisms.

Acoustic Stimulation↗
PubMed1992

Single evoked potential reconstruction by means of wavelet transform.

We would like to propose a method of single evoked potential (EP) extraction free from assumptions and based on a novel approach--the wavelet representation of the signal. Wavelets were introduced by Grossman and Morlet in 1984. The method is based on the multiresolution signal decomposition. Wavelets are already used for speech recognition, geophysics investigations and fractal analysis. This method seems to be a useful improvement upon Fourier Transform analysis, since it provides simultaneous information on frequency and time localization of the signal. We would like to introduce wavelet formalism for the first time to brain signal analysis. One of the most important problems in this field is the analysis of evoked potentials. This signal has an amplitude several times smaller than EEG, therefore stimulus-synchronized averaging is commonly used. This method is based on several assumptions. Namely it is postulated that: 1) EP are characterized by a deterministic repeatable pattern, 2) EEG has purely stochastic character, 3) EEG and EP are independent. These assumptions have been challenged e.g. the variability of the EP pattern was demonstrated by John (1973) by means of factor analysis. In view of the works of Sayers et al. (1974) and Başar (1988) EP reflects the reorganization of the spontaneous activity under the influence of a stimulus and it is connected with the redistribution of EEG phases. Several attempts to overcome the limitation of the averaging method have been made. Heintze and Künkel (1984) used an autoregressive moving average (ARMA) model to extract evoked potentials from 2 segments. This was possible under two conditions: high signal to noise ratio and clear separation of the EEG and EP spectra.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed1991

A new method of the description of the information flow in the brain structures.

The paper describes the method of determining direction and frequency content of the brain activity flow. The method was formulated in the framework of the AR model. The transfer function matrix was found for multichannel EEG process. Elements of this matrix, properly normalized, appeared to be good estimators of the propagation direction and spectral properties of the investigated signals. Simulation experiments have shown that the estimator proposed by us unequivocally reveals the direction of the signal flow and is able to distinguish between direct and indirect transfer of information. The method was applied to the signals recorded in the brain structures of the experimental animals and also to the human normal and epileptic EEG. The sensitivity of the method and its usefulness in the neurological and clinical applications was demonstrated.

Animals↗
PubMed1991

Non-linear and linear forecasting of the EEG time series.

The method of non-linear forecasting of time series was applied to different simulated signals and EEG in order to check its ability of distinguishing chaotic from noisy time series. The goodness of prediction was estimated, in terms of the correlation coefficient between forecasted and real time series, for non-linear and autoregressive (AR) methods. For the EEG signal both methods gave similar results. It seems that the EEG signal, in spite of its chaotic character, is well described by the AR model.

Algorithms↗
PubMed1989

The EEG time series parametrization method in the study of nociception.

A new method of EEG time series parametrization was proposed. The EEG rhythms were described in terms of frequency, amplitude and damping. These parameters were used in multivariate analysis of variance in order to estimate the group differences in terms of Mahalanobis distances. The rhythm diagrams were constructed and their advantages in comparison with averaged power spectra were discussed. The proposed method overcomes the difficulties connected with the arbitrary division of spectrum into the frequency bands. The usefulness of the method in the study of nociception was tested. The effect of nociceptive stimulation in the four brain structures: sensorimotor cortex, ventro-postero-lateral thalamic nuclei, midbrain reticular formation and periaqueductal gray was investigated. The influence of atropine and mecamylamine administered locally was studied.

Algorithms↗
PubMed1988

A new method of presentation of the average spectral properties of the EEG time series.

In framework of the AR model the transfer function and the impulse response function of the EEG time series were found. The parameters of the impulse response function were interpreted in terms of: frequencies, damping factors and amplitudes of the hypothetical brain rhythm generators. The method of the rhythm diagrams was proposed to describe the spectral properties of the EEG time series. It makes possible the accurate estimation of the EEG rhythm's frequencies and their intensities, especially weak components hardly visible in the conventional spectral analysis are well distinguished. Rhythm diagrams can be a useful tool in the clinical applications and offer new possibilities in the direct comparison of the results of modeling with the experimental data.

Animals↗
PubMed1987

A study of stability of electrocortical rhythm generators.

By means of the autoregressive model the transfer function and the impulse response function were determined and the parameters characterizing electrocortical oscillators were found for the four brain structures of the experimental animals. Different kinds of the representation of these parameters were compared and their sensitivity to the different factors was tested. The advantages of the proposed method over the conventional spectral analysis were demonstrated. Cluster analysis was applied in order to test the stability of the determined basic rhythms. Proposed method seems to be very useful for the investigation of the processes of rhythmical activity generation and control.

Animals↗
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↗