Characteristic of Mayer Waves in Electrophysiological, Hemodynamic and Vascular Signals.

We evaluated the properties of oscillations in the Mayer waves (MW) frequency range (∼0.1Hz) detected in blood pressure, heart rate variability, cerebral blood oxygenation changes and evolution of electroencephalographic (EEG) rhythms to elucidate the mechanisms of MW generation. We examined the persistence of MW in different signals and stability of their oscillations on the level of individual MW waveforms, which was achieved by applying matching pursuit (MP). MP yields adaptive time-frequency approximation of signal's structures in terms of frequency, amplitude, time occurrence, and time-span. The number of waveforms contributing to 95% of the energy of the signals was vastly different for the time series, but the average number of waveforms conforming to the MW criteria was almost the same (3.5 ± 0.4 per 120s epoch). In all the investigated signals, MW had the same distributions of frequency and the number of cycles. We show that the MW energy ratios in different signals varied strongly, p < 0.001. The highest percentage of MW energy was observed in blood pressure signals, heart rate variability, and reduced hemoglobin, in contrast to brain signals and oxygenated hemoglobin. The percentage of MW energy was related to the strength of causal influence exerted by them on the other signals. Our results indicate existence of a common mechanism of MW generation and support the hypothesis of MW generation in the baroreflex loop; however, they do not exclude the action of a central pacemaker.

Causal Coupling Between Electrophysiological Signals, Cerebral Hemodynamics and Systemic Blood Supply Oscillations in Mayer Wave Frequency Range.

The aim of the study was to assess causal coupling between neuronal activity, microvascular hemodynamics and blood supply oscillations in the Mayer wave frequency range. An electroencephalogram, cerebral blood oxygenation changes, an electrocardiogram and blood pressure were recorded during rest and during a movement task. Causal coupling between them was evaluated using directed transfer function, a measure based on the Granger causality principle. The multivariate autoregressive model was fitted to all the signals simultaneously, which made it possible to construct a complete scheme of interactions between the considered signals. The obtained pattern of interactions in the resting state estimated in the 0.05-0.15 Hz band revealed a predominant influence of blood pressure oscillations on all the other variables. Reciprocal connections between blood pressure and heart rate variability time series indicated the presence of feedback loops between these signals. During movement, the pattern of connections did not change dramatically. The number of connections decreased, but the couplings between blood pressure and heart rate variability signal were not significantly changed, and the strong influence of the decreased blood hemoglobin concentration on the oxygenated hemoglobin concentration persisted. For the first time our results provided a comprehensive scheme of interactions between electrical and hemodynamic brain signals, heart rate and blood pressure oscillations. Persistent reciprocal connections between blood pressure and heart rate variability time series suggest possible feedforward and feedback coupling of cardiovascular variables which may lead to the observed oscillations in Mayer wave range.

Information infrastructure for cooperative research in neuroscience.

The paper describes a framework for efficient sharing of knowledge between research groups, which have been working for several years without flaws. The obstacles in cooperation are connected primarily with the lack of platforms for effective exchange of experimental data, models, and algorithms. The solution to these problems is proposed by construction of the platform (EEG.pl) with the semantic aware search scheme between portals. The above approach implanted in the international cooperative projects like NEUROMATH may bring the significant progress in designing efficient methods for neuroscience research.

Multivariate autoregressive model for a study of phylogenetic diversity.

We present a computationally effective model to parameterize DNA sequences in a way describing comprehensively its auto and cross-correlation structure. The approach is based on four-channel Multivariate Autoregressive Model (MVAR). The model was applied to a study of genes from the globin family for 6 vertebrate species. First, the sequences were coded as four signals (corresponding to the nucleotides), which were fitted to a four-channel MVAR. From the correlation matrices the vectors of model coefficients were calculated as functions of the nucleotide distance. The between-chromosomes and inter-species differences were best distinguished in the cross-coefficients binding different nucleotide sequences. For clustering purposes different metrics were tested and then two clustering procedures (Nearest Neighbor and UPGMA) were applied. The clustering trees and consensus trees were constructed for exons, introns and whole genes. The results were in agreement with the known dependencies between the chromosomes of the globin family. The orthological genes for different species were grouped together. Inside these groups the phylogenetically close organisms were localized in proximity.

Efficient application of Internet databases for new signal processing methods.

This paper highlights the ways in which Internet databases may be efficiently used to foster the application of progress in biomedical sciences via data sharing and new algorithms. Employing the Internet to accelerate the pace of interdisciplinary research has significant potential, yet as with all new technologies, the first applications often cause more disappointment than positive outcomes. We discuss examples of solutions to the basic issues: (1) finding the relevant datasets (in portals connected via the Inter-neuro infrastructure), (2) reading the particular format in which the data was stored (using the SignalML language for metadescription of time series), (3) choosing the right method for the data analysis (we provide a brief review of the methods used for the analysis of EEGs, and discuss two of them in detail: Directed Transfer Function and Matching Pursuit), and (4) sharing the software for chosen methods of analysis (via repositories such as the eeg.pl thematic portal).

Propagation of EEG activity in the beta and gamma band during movement imagery in humans.

OBJECTIVES: The objective of the paper was the determination of electrical brain activity propagation in sensorimotor areas during hand movement imagery. METHODS: Right-hand and left-hand movement imagination was studied in three subjects. The 10-channel Multivariate Autoregressive Model (MVAR) was fitted to EEG signals recorded from subsets of electrodes overlying central and related brain areas. By means of the Short-time Directed Transfer Function (SDTF) the propagation of brain activity as a function of frequency and time was found. RESULTS: During imagery the relation between propagations in gamma and beta bands changed significantly for electrodes overlying sensorimotor areas, namely the increase in gamma was accompanied by the decrease in the beta band. CONCLUSIONS: The hypothesis was put forward that these kinds of changes in flow of electrical brain activity are connected with the specific information processing.

Time-frequency analysis of brain electrical activity--adaptive approximations.

OBJECTIVES: We present an approach to time-frequency analysis of bioelectrical signals. METHODS: The method relays on the decomposition of the signal into a set of waveforms that have good localization both in time and in frequency. The waveforms belong to a highly redundant set of functions - allowing for a very accurate description of signal components. RESULTS: Properties of the method are illustrated by simulations and applications to EEG. CONCLUSION: The presented method delivers a common formalism suitable for describing both gross statistical properties of structures present in bioelectrical signals, as well as microstructure of chosen phenomena.

A simple system for detection of EEG artifacts in polysomnographic recordings.

We present an efficient parametric system for automatic detection of electroencephalogram (EEG) artifacts in polysomnographic recordings. For each of the selected types of artifacts, a relevant parameter was calculated for a given epoch. If any of these parameters exceeded a threshold, the epoch was marked as an artifact. Performance of the system, evaluated on 18 overnight polysomnographic recordings, revealed concordance with decisions of human experts close to the interexpert agreement and the repeatability of expert's decisions, assessed via a double-blind test. Complete software (Matlab source code) for the presented system is freely available from the Internet at http://brain.fuw.edu.pl/artifacts.

Unbiased high resolution method of EEG analysis in time-frequency space.

Matching Pursuit (MP)--a method of high-resolution signal analysis--is described in the context of other methods operating in time-frequency space. The method relies on an adaptive approximation of a signal by means of waveforms chosen from a very large and redundant dictionary of functions. The MP performance is illustrated by simulations and examples of sleep spindles and slow wave activity analysis. An improvement of the original procedure, relying on the introduction of stochastic dictionaries, is proposed. A comparison of the performance of dyadic and stochastic dictionaries is presented. MP with stochastic dictionaries is characterized by an unmatched resolution in time-frequency space; moreover it allows for parametric description of all (periodic and transient) signal features in the framework of the same formalism. Matching pursuit is especially suitable for analysis of non-stationary signals and is a unique tool for the investigation of dynamic changes of brain activity.

Phase and amplitude analysis in time-frequency space--application to voluntary finger movement.

Two methods operating in time-frequency space were applied to analysis of EEG activity accompanying voluntary finger movements. The first one, based on matching pursuit approach provided high-resolution distributions of power in time-frequency space. The phenomena of event related desynchronization (ERD) and synchronization (ERS) were investigated without the need of band-pass filtering. Time evolution of mu- and beta-components was observed in a detailed way. The second method was based on a multichannel autoregressive model (MVAR) adapted for investigation of short-time changes in EEG signal. The direction and spectral content of the EEG activity propagation was estimated by means of short-time directed transfer function (SDTF). The evidence of 'cross-talk' between different areas of motor and sensory cortex was found. The earlier known phenomena, connected with voluntary movements, were confirmed and a new evidence concerning focal ERD/surround ERS and beta activity post-movement synchronization was found.

High resolution study of sleep spindles.

OBJECTIVE: Universal high-resolution time-frequency parameterization of sleep EEG structures. METHODS: A new algorithm called Matching Pursuit was used for the decomposition of sleep EEG into waveforms chosen from a large and redundant set of functions. As a result all signal structures were parameterized in terms of their frequency, time occurrence, time span and energy. Slow wave activity and sleep spindles were identified according to neurophysiological criteria and various distributions describing their time evolution, topographical and frequency characteristics were constructed. RESULTS: Two types of sleep spindles of different topological and spectral properties were identified. High time-frequency resolution made possible separation of superimposed spindles. Cross-correlation between high- and low-frequency components of superimposed spindles revealed a fixed time-delay between them, the high-frequency component preceding the low-frequency one. CONCLUSION: The results of our study suggest that processes of generation of both types of sleep spindles are weakly coupled.

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.

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.

Topographic analysis of coherence and propagation of EEG activity during sleep and wakefulness.

Overnight sleep EEG recorded from 21 derivations was studied in 8 healthy subjects. The vector autoregressive model was fitted to all 21 channels simultaneously. Ordinary, multiple and partial coherences and directed transfer functions were estimated for sleep stages and wakefulness. Ordinary coherences give rather trivial information that coherence decreases with distance. Partial coherences revealed specific structure that was well repeatable for the subjects studied. Differences in coherence patterns between sleep stages were found by means of statistical tests. An increase of coherence was found for sleep stages 2, 3 and 4. Directed transfer function made possible the identification of the main centers from which EEG activity is spreading during sleep and wakefulness. During sleep the influence of subcortical structures was manifested by propagation of activity from the fronto-central region. The range of this interaction was highest in sleep stages 3 and 4. An EEG analysis, based on the approach of treating time series as a realization of one process and on the simultaneous (not pair-wise) evaluation of signals offers new possibilities in the investigation of synchronization and functional relations in the brain.

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.

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.

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.

Investigation of coherence structure and EEG activity propagation during sleep.

Overnight sleep EEG recorded from 21 derivations was studied for 7 subjects (4 normal and 3 depressive). The multichannel autoregressive model was fitted to all 21 channels simultaneously. Ordinary, multiple and partial coherencies and directed transfer function were estimated for sleep stages and wakefulness. Ordinary coherencies give rather trivial information that coherence decreases with the distance. Partial coherencies revealed specific structure to a large degree repeatable for studied subjects. Study of directed transfer function made possible the identification of main centres from which EEG activity is spreading during sleep. An EEG analysis, based on treating signals as a realization of one process and on simultaneous (not pair-wise) evaluation of time series, offers new possibilities in the investigation of synchronization and functional relations in brains.