Vadim V. Grubov

Time-frequency characteristics and dynamics of sleep spindles in WAG/Rij rats with absence epilepsy.

In rat models of absence epilepsy, epileptic spike-wave discharges appeared in EEG spontaneously, and the incidence of epileptic activity increases with age. Spike-wave discharges and sleep spindles are known to share common thalamo-cortical mechanism, suggesting that absence seizures might affect some intrinsic properties of sleep spindles. This paper examines time-frequency EEG characteristics of anterior sleep spindles in non-epileptic Wistar and epileptic WAG/Rij rats at the age of 7 and 9 months. Considering non-stationary features of sleep spindles, EEG analysis was performed using Morlet-based continuous wavelet transform. It was found, first, that the average frequency of sleep spindles in non-epileptic Wistar rats was higher than in WAG/Rij (13.2 vs 11.2 Hz). Second, the instantaneous frequency ascended during a spindle event in Wistar rats, but it was constant in WAG/Rij. Third, in WAG/Rij rats, the number and duration of epileptic discharges increased in a period between 7 and 9 months of age, but duration and mean value of intra-spindle frequency did not change. In general, age-dependent aggravation of absence seizures in WAG/Rij rats did not affect EEG properties of sleep spindles; it was suggested that pro-epileptic changes in thalamo-cortical network in WAG/Rij rats might prevent dynamic changes of sleep spindles that were detected in Wistar.

Coexistence of intermittencies in the neuronal network of the epileptic brain.

Intermittent behavior occurs widely in nature. At present, several types of intermittencies are known and well-studied. However, consideration of intermittency has usually been limited to the analysis of cases when only one certain type of intermittency takes place. In this paper, we report on the temporal behavior of the complex neuronal network in the epileptic brain, when two types of intermittent behavior coexist and alternate with each other. We prove the presence of this phenomenon in physiological experiments with WAG/Rij rats being the model living system of absence epilepsy. In our paper, the deduced theoretical law for distributions of the lengths of laminar phases prescribing the power law with a degree of -2 agrees well with the experimental neurophysiological data.

Age-Dependent Increase of Absence Seizures and Intrinsic Frequency Dynamics of Sleep Spindles in Rats.

The risk of neurological diseases increases with age. In WAG/Rij rat model of absence epilepsy, the incidence of epileptic spike-wave discharges is known to be elevated with age. Considering close relationship between epileptic spike-wave discharges and physiologic sleep spindles, it was assumed that age-dependent increase of epileptic activity may affect time-frequency characteristics of sleep spindles. In order to examine this hypothesis, electroencephalograms (EEG) were recorded in WAG/Rij rats successively at the ages 5, 7, and 9 months. Spike-wave discharges and sleep spindles were detected in frontal EEG channel. Sleep spindles were identified automatically using wavelet-based algorithm. Instantaneous (localized in time) frequency of sleep spindles was determined using continuous wavelet transform of EEG signal, and intraspindle frequency dynamics were further examined. It was found that in 5-months-old rats epileptic activity has not fully developed (preclinical stage) and sleep spindles demonstrated an increase of instantaneous frequency from beginning to the end. At the age of 7 and 9 months, when animals developed matured and longer epileptic discharges (symptomatic stage), their sleep spindles did not display changes of intrinsic frequency. The present data suggest that age-dependent increase of epileptic activity in WAG/Rij rats affects intrinsic dynamics of sleep spindle frequency.

Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks

In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces.

Visual perception affected by motivation and alertness controlled by a noninvasive brain-computer interface

The influence of motivation and alertness on brain activity associated with visual perception was studied experimentally using the Necker cube, which ambiguity was controlled by the contrast of its ribs. The wavelet analysis of recorded multichannel electroencephalograms (EEG) allowed us to distinguish two different scenarios while the brain processed the ambiguous stimulus. The first scenario is characterized by a particular destruction of alpha rhythm (8–12 Hz) with a simultaneous increase in beta-wave activity (20–30 Hz), whereas in the second scenario, the beta rhythm is not well pronounced while the alpha-wave energy remains unchanged. The experiments were carried out with a group of financially motivated subjects and another group of unpaid volunteers. It was found that the first scenario occurred mainly in the motivated group. This can be explained by the increased alertness of the motivated subjects. The prevalence of the first scenario was also observed in a group of subjects to whom images with higher ambiguity were presented. We believe that the revealed scenarios can occur not only during the perception of bistable images, but also in other perceptual tasks requiring decision making. The obtained results may have important applications for monitoring and controlling human alertness in situations which need substantial attention. On the base of the obtained results we built a brain-computer interface to estimate and control the degree of alertness in real time.

Time-frequency analysis of epileptic EEG patterns by means of empirical modes and wavelets

In this paper we perform a time-frequency analysis of epileptic EEG patterns based on two approaches for characterizing nonstationary multi-frequency signals, namely, the continuous wavelet transform (CWT) and the empirical mode decomposition (EMD). Possibilities and limitations of both these techniques are considered, and a combined approach for automatic pattern detection is proposed.

Adaptive wavelet transform-based method for recognizing characteristic oscillatory patterns

The problem concerning the automatic recognition of characteristic oscillatory patterns in multicomponent signals is investigated using the brain’s electric activity records, electroencephalograms (EEGs), as an example. It has been ascertained that recognition errors can be decreased by optimally selecting continuous wavelet transform (CWT) parameters to obtain characteristics describing the most important information on analyzed patterns. The adaptive CWT-based method for identifying the characteristic types of EEG rhythmic activity is proposed.

Automatic Extraction and Analysis of Oscillatory Patterns on Nonstationary EEG Signals by Means of Wavelet Transform and the Empirical Modes Method

The time-frequency structure and dynamics of oscillatory patterns in electroencephalograms of rats is studied by means of continuous wavelet transform and the decomposition of the signal by empirical modes. A method for the automatic selection of patterns using the empirical modes is developed. The method is applied to the study of sleep spindles, and it is shown that their dynamics depends on the regularities of on-off intermittency.

Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms

The problem of automatic recognition of specific oscillatory patterns on electroencephalograms (EEG) is addressed using the continuous wavelet-transform (CWT). A possibility of improving the quality of recognition by optimizing the choice of CWT parameters is discussed. An adaptive approach is proposed to identify sleep spindles (SS) and spike wave discharges (SWD) that assumes automatic selection of CWT-parameters reflecting the most informative features of the analyzed time-frequency structures. Advantages of the proposed technique over the standard wavelet-based approaches are considered.

Adaptive filtering of electroencephalogram signals using the empirical-modes method

A new method for the removal of physiological artifacts in the experimental signals of human electroencephalograms (EEGs) has been developed. The method is based on decomposition of the signal in terms of empirical modes. The algorithm involves EEG signal decomposition in terms of empirical modes, searching for modes with artifacts, removing these modes, and restoration of the EEG signal. The method was tested on experimental data and showed high efficiency in the removal of various physiological artifacts in EEGs.