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P J Franaszczuk

Publications and source records attributed to P J Franaszczuk.

18 recordsLinked to original sources
PubMed2006

Estimation of short-time cross-correlation between frequency bands of event related EEG.

Simultaneous variations of the event-related power changes (ERD/ERS) are often observed in a number of frequency bands. ERD/ERS measures are usually based on the relative changes of power in a given single frequency band. Within such an approach one cannot answer questions concerning the mutual relations between the band-power variations observed in different frequency bands. This paper addresses the problem of estimating and assessing the significance of the average cross-correlation between ERD/ERS phenomena occurring in two frequency bands. The cross-correlation function in a natural way also provides estimation of the delay between ERD/ERS in those bands. The proposed method is based on estimating the short-time cross-correlation function between relative changes of power in two selected frequency bands. The cross-correlation function is estimated in each trial separately and then averaged across trials. The significance of those mean cross-correlation functions is evaluated by means of a nonparametric test. The basic properties of the method are presented on simulated signals, and an example application to real EEG and ECoG signals is given.

Adult↗
PubMed2005

Computationally efficient approaches to calculating significant ERD/ERS changes in the time-frequency plane.

This paper addresses some practical issues related to the calculation, display and assessment of the significance of changes in the average time-frequency energy density of event-related brain activity. Using scalp EEG and subdural ECoG example datasets, parametric tests are evaluated as a replacement for previously applied computer-intensive resampling methods. The performance of different estimates of energy density, based on matching pursuit, scalogram and spectrogram, and their Box-Cox transformations is evaluated with respect to the assumption of normality required for the t-test, and the consistency of the final results.

Adult↗
PubMed2001

Epileptic seizures are characterized by changing signal complexity.

OBJECTIVE: Epileptic seizures are brief episodic events resulting from abnormal synchronous discharges from cerebral neuronal networks. The traditional methods of signal analysis are limited by the rapidly changing nature of the EEG signal during a seizure. Time-frequency analyses, however, such as those produced by the matching pursuit (MP) method can provide continuous decompositions of recorded seizure activity. These accurate decompositions can allow for more detailed analyses of the changes in complexity of the signal that may accompany seizure evolution. METHODS: The MP algorithm was applied to provide time-frequency decompositions of entire seizures recorded from depth electrode contacts in patients with intractable complex partial seizures of mesial temporal onset. The results of these analyses were compared with signals generated from the Duffing equation that represented both limit cycle and chaotic behavior. RESULTS: Seventeen seizures from 12 different patients were analyzed. These analyses reveal that early in the seizure, the most organized, rhythmic seizure activity is more complex than limit cycle behavior, and that signal complexity increases further later in the seizure. CONCLUSIONS: Increasing complexity routinely precedes seizure termination. This may reflect progressive desynchronization.

Algorithms↗
PubMed1999

An autoregressive method for the measurement of synchronization of interictal and ictal EEG signals.

We propose a new measure of synchronization of multichannel ictal and interictal EEG signals. The measure is based on the residual covariance matrix of a multichannel autoregressive model. A major advantage of this measure is its ability to be interpreted both in the framework of stochastic and deterministic models. A preliminary analysis of EEG data from three patients using this measure documents the expected increased synchronization during ictal periods but also reveals that increased synchrony persists for prolonged periods (up to 2 h or more) in the postictal period.

Electroencephalography↗
PubMed1998

Time-frequency analysis using the matching pursuit algorithm applied to seizures originating from the mesial temporal lobe.

OBJECTIVES: The ability to analyze patterns of recorded seizure activity is important in the localization and classification of seizures. Ictal evolution is typically a dynamic process with signals composed of multiple frequencies; this can limit or complicate methods of analysis. The recently-developed matching pursuit algorithm permits continuous time-frequency analyses, making it particularly appealing for application to these signals. The studies here represent the initial applications of this method to intracranial ictal recordings. METHODS: Mesial temporal onset partial seizures were recorded from 9 patients. The data were analyzed by the matching pursuit algorithm were continuous digitized single channel recordings from the depth electrode contact nearest the region of seizure onset. Tine frequency energy distributions were plotted for each seizure and correlated with the intracranial EEG recordings. RESULTS: Periods of seizure initiation, transitional rhythmic bursting activity, organized rhythmic bursting activity and intermittent bursting activity were identified. During periods of organized rhythmic bursting activity, all mesial temporal onset seizures analyzed had a maximum predominant frequency of 5.3-8.4 Hz with a monotonic decline in frequency over a period of less than 60 s. The matching pursuit method allowed for time-frequency decomposition of entire seizures. CONCLUSIONS: The matching pursuit method is a valuable tool for time-frequency analyses of dynamic seizure activity. It is well suited for application to the non-stationary activity that typically characterizes seizure evolution. Time-frequency patterns of seizures originating from different brain regions can be compared using the matching pursuit method.

Algorithms↗
PubMed1998

Application of the directed transfer function method to mesial and lateral onset temporal lobe seizures.

The directed transfer function (DTF) method is a multichannel analysis based on an autoregressive model that detects flow of seizure activity. This report extends the application of the DTF method to compare patterns of flow of seizures with different sites of origin. Analysis of a seizure originating from mesial temporal structures is compared with a seizure originating from lateral temporal neocortex; both complex partial seizures were recorded with intracranial electrodes that combine subdural grid arrays and depth electrodes. The DTF method has the potential to determine patterns of flow of activity, including periods when visual analysis of the intracranial ictal EEG may not allow for definitive source localization. The extension of the DTF analyses into integrated DTF (IDTF) formats is also illustrated. When activity of a relatively discrete frequency can be identified, the IDTF analysis facilitates display of patterns of flow of this selected activity.

Algorithms↗
PubMed1997

A general statistical framework for frequency-domain analysis of EEG topographic structure.

A wide variety of rhythmic electrophysiological phenomena--including driven, induced, and endogenous activities of cortical neuronal masses--lend themselves naturally to analysis using frequency--domain techniques applied to multichannel recordings that discretely sample the overall spatial pattern of the rhythmic activity. For such cases, a large but so far poorly utilized body of statistical theory supports a third major approach to topographic analysis, complementing the more familiar mapping and source-recovery techniques. These methods, many of which have only recently become computationally feasible, collectively provide general solutions to the problem of detecting and characterizing systematic differences that arise--not only in the spatial distribution of the activity, but also in its frequency-dependent between-channel covariance structure--as a function of multiple experimental conditions presented in conformity with any of the conventional experimental designs. This application-oriented tutorial review provides a comprehensive outline of these resources, including: (1) real multivariate analysis of single-channel spectral measures (and measures of between-channel relationships such as coherence and phase), (2) complex multivariate analysis based on multichannel Fourier transforms, and (3) complex multivariate analysis based on multichannel parametric models. Special emphasis is placed on the potential of the multichannel autoregressive model to support EEG (and MEG) studies of perceptual and cognitive processes.

Cerebral Cortex↗
PubMed1997

A simple computer model of excitable synaptically connected neurons.

The space-lumped two-variable neuron model is studied. Extension of the neural model by adding a simple synaptic current allows the demonstration of neural interactions. The production of synchronous burst activity in this simple two-neuron excitatory loop is modeled, including the influence of random background excitatory input. The ability of the neuron model to integrate inputs spatially and temporally is shown. Two refractory periods after stimuli were identified and their role in burst cessation is demonstrated. Our findings show that simple neural units without long-lasting membrane processes are capable of generating long lasting patterns of activity. The results of simulation of simple background activity suggest that an increase in background activity tends to cause decreased activity of the network. This phenomenon, as well as the existence of two refractory periods, allows for burst cessation without inhibition in this simple model.

Neural Networks, Computer↗
PubMed1995

Detection of spontaneous postsynaptic potentials.

The amplitude and time course of postsynaptic potentials (PSPs) recorded by intracellular techniques contain information that allow different synaptic events to be detected. In the present paper an algorithm to detect spontaneous PSPs is described. The algorithm is based on computation of approximations of first and second derivatives of the signals. The method was tested on both computer-simulated potentials and on experimental data recorded from dissociated mouse spinal cord neurons in tissue culture. The receiver operating characteristics of the detection algorithm were computed. This method can be applied to investigations of dynamic changes in the activity of neural networks.

Algorithms↗
PubMed1994

Analysis of mesial temporal seizure onset and propagation using the directed transfer function method.

The directed transfer function (DTF) method, a multichannel parametric method of analysis based on an autoregressive model, is a newly developed tool that permits determination of patterns of flow of activity. The DTF method of analysis was applied to seizures originating from mesial temporal lobe structures in 3 patients recorded by combined subdural grid and depth electrode arrays. These first applications to human intracranial recordings demonstrated that the DTF method can accurately determine patterns of seizure onset and propagation. In addition the DTF method can provide evidence regarding patterns of flow of seizure activity that are not readily apparent from visual inspection of the EEG recordings. Important considerations for appropriate application of the DTF method for the analysis of intracranial ictal recordings are discussed.

Brain↗
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

FAD-parametric description of EEG time series.

A new method of the EEG time series parametric description was elaborated. The procedure consists of the following steps: calculation of AR model coefficients, the estimation of the transfer function in the form of parallel filters, finding of the impulse response function parameters. The parameters: frequencies, amplitudes and damaging coefficients (FAD) characterise the basic EEG waves. The average properties of the EEG signals are presented in the form of rhythm diagrams. The method overcomes the difficulties in arbitrary partitioning of the EEG power spectrum into the frequency bands and makes possible the reduction of electroencephalographic data to the few parameters characterising basic rhythms.

Electroencephalography↗