Dynamics of the reactions of nonspecific and somatic activation while learning adaptive biocontrol

Dynamics of characteristics of respiration, cardiac, muscular and EEG-activities was studied in volunteers during their learning of voluntary control of skin resistance and temperature by means of biological feedback. Initial stage of learning of adaptive biocontrol was connected with involvement of systemic activation reactions, which had similar characteristics during control of various vegetative parameters both at their increase and decrease. The revealed activation shifts represent non-specific reaction characteristic of learning and extinction at gradual specialization and consolidation of control habit. Characteristics of learning dynamics confirmed N. Miller's hypothesis that adaptive biocontrol represents a form of instrumental conditioning. At the same time, the involvement of systemic reactions is not an obligatory condition of adaptive biocontrol learning. This fact confirms the presence of various "individual strategies" of learning and existence of a sufficiently wide spectrum of physiological mechanisms of visceral processes central control which could be included at learning of voluntary control of visceral parameters.     

Adaptive prediction of epileptic seizures from intracranial recordings

Previous work has demonstrated that some dynamic properties of intracranial EEG signals are indicative of epileptic seizures and hence could be used for prediction in order to realize counter measures. However, most previous studies only investigated predictability via offline analysis of EEG signals as compared to actually predicting seizures in a setting applicable to implantable devices. Here we address this problem, which calls for simple and fast online methods, and based on previous offline analyses we hypothesize that prediction can be further improved when using multiple features to detect the preictal patterns. We propose a simple adaptive online method (an evolving neuro-fuzzy model) to adaptively learn such combined features. The classifier starts out with a simple structure and patient-independent parameters and then grows into a personal seizure predictor as recursive methods tune the model structure and parameters. We apply the adaptive classifier to a publicly available database of intracranial recordings from 21 patients and demonstrate that seizure prediction is improved with our online method as compared to offline non-adaptive techniques. We show that our method is robust with respect to those few model parameters, which are not adapted. Moreover, as we report the performance on data from a publicly available seizure database, our results can serve as a yardstick for future method developments.     

Retention of the skill of adaptive biocontrol of cortical bioelectric activity synchronization in argon-nitrogen-oxygen atmosphere with different oxygen concentrations

A decreased oxygen concentration in the air (hypoxia) is an efficient preventive measure against fire in spacecrafts and other habitable pressurized modules. Addition of argon to the air may increase the human tolerance of this hypoxia. In order to estimate the human mental performance in argon-nitrogenoxygen atmosphere (ANOA) with different oxygen concentrations, the following characteristics were determined: (i) the retention of the skill of adaptive biocontrol (ABC) of bioelectric activity synchronization in the frontoparietal and parietooccipital cortical areas with the use of EEG; (ii) emotional and mental states estimated using Luscher’s test; and (iii) performance parameters estimated using the computer games Tetris and Minesweeper as models. Despite fluctuations of the anxiety level, ANOA did not decrease the parameters of the performance of the model activity or long-term retention of the ABC skill (for 24 h), specific characteristics of the ABC skill retention being related to the type of activity.     

Efficacy of EEG biofeedback procedures in correcting stress-related functional disorders

A promising approach to nondrug correction of human stress-induced functional disorders based on double EEG biofeedback (EEGBF) has been substantiated and experimentally tested. According to this approach, narrow-band EEG oscillators that are characteristic of each patient and detectable in real-time are simultaneously used in two independent feedback loops: the traditional adaptive biofeedback loop and an additional resonance stimulation loop. In the latter loop, the feedback signals from individual narrow-band EEG oscillators serve for automatic modulation of the parameters of sensory stimuli and are not perceived consciously by the subject. The combined use of the active (conscious perception) and passive (automatic modulation) feedback signals from narrow-band EEG components of the patient have been demonstrated to offer the possibility of a substantial increase in the efficacy of EEGBF.     

Optimized Tunable Q Wavelet Transform Based Drowsiness Detection from Electroencephalogram Signals

Early discernment of drivers drowsy state may prevent numerous worldwide road accidents. Electroencephalogram (EEG) signals provide valuable information about the neurological changes for discrimination of alert and drowsy state. A signal is decomposed into multi-components for the analysis of the physiological state. Tunable Q wavelet transform (TQWT) decomposes the signal into low-pass and high-pass sub-bands without a choice of wavelet. The information content captured by these sub-bands depends on the choice of decomposition parameters. Due to the non-stationary nature of EEG signals, the predefined decomposition parameters of TQWT lead to information loss and degrade system performance. Hence it is required to automate the decomposition parameters in accordance with the nature of signals. In this paper, an optimized tunable Q wavelet transform (O-TQWT) is proposed for the adaptive selection of decomposition parameters by using different optimization algorithms. Objective function as a mean square error (MSE) of decomposition is minimized by optimization algorithms. Optimum decomposition parameters are used to decompose the signals into sub-bands. Time-domain based features are excerpted from the sub-bands of O-TQWT. Highly discriminant features selected by using Kruskal Wallis test are used as an input to different classification techniques. Classification accuracy of 96.14% is achieved by least square support vector machine with radial basis function kernel which is better than the other existing methodologies using the same database.     

The age-related characteristics of the organization of brain electrical activity in 7- to 14-year-old schoolchildren with neuroses in different functional states

By the method of factor analysis characteristics of functional brain states in children and juveniles of different age with neuroses were studied by EEG data. Similarity was established of the patients EEG structure at strained and emotional states. In the studied period of individual development (7-14 years) the most expressed changes of background EEG were observed at the age og 9-12 years. At functional loads, beginning from the age of 9-10, insufficient activity of the frontal region of the left hemisphere was found. The greatest changes of the EEG integral characteristics were observed in patients with vegetative-emotional disturbances and the smallest-at verbal-motor ones.     

Topographic analysis of dimension estimates of EEG and filtered rhythms in epileptic patients with complex partial seizures

Nonlinear dynamic properties were analyzed on the EEG and filtered rhythms recorded from healthy subjects and epileptic patients with complex partial seizures. Estimates of correlation dimensions of control EEG, interictal EEG and ictal EEG were calculated. The values were demonstrated on topograms. The delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–40 Hz) components were obtained and considered as signals from the cortex. Corresponding surrogate data was produced. Firstly, the influence of sampling parameters on the calculation was tested. The dimension estimates of the signals from the frontal, temporal, parietal and occipital regions were computed and compared with the results of surrogate data. In the control subjects, the estimates between the EEG and surrogate data did not differ (P > 0.05). The interictal EEG from the frontal region and occipital region, as well as its theta component from the frontal region, and temporal region, showed obviously low dimensions (P < 0.01). The ictal EEG exhibited significantly low-dimension estimates across the scalp. All filtered rhythms from the temporal region yielded lower results than those of the surrogate data (P < 0.01). The dimension estimates of the EEG and filtered components markedly changed when the neurological state varied. For each neurological state, the dimension estimates were not uniform among the EEG and frequency components. The signal with a different frequency range and in a different neurological state showed a different dimension estimate. Furthermore, the theta and alpha components demonstrated the same estimates not only within each neurological state, but also among the different states. These results indicate that the theta and alpha components may be caused by similar dynamic processes. We conclude that the brain function underlying the ictal EEG has a simple mechanism. Several heterogeneous dynamic systems play important roles in the generation of EEG. Received: 10 December 1999 / Accepted in revised form: 8 May 2000     

The role of cerebral regulatory structures in the formation of human EEG

This article generalizes the results of many years’ studies of the EEG of patients with tumorous lesions in the diencephalic, brainstem, and limbic structures, which fulfill the regulatory function in ensuring integral brain activity. The specific features of the inclusion of individual structures under investigation in the organization of the intra- and interhemispheric relations of cortical biopotentials were demonstrated against the background of diffuse changes in the biopotentials that reflect the systemic character of neurodynamic reorganizations when the regulatory brain structures are involved in the pathological process. This study expands the idea of the predominant functional connection of the diencephalic structures with the right hemisphere and brainstem structures with the left one with determination of the regional specific features of changes in the intrahemispheric EEG coherences. The distinguishing features of intercentral relations when the limbic structures are involved in the pathological process show similarity with the neurodynamic reorganizations in patients with lesions in both diencephalic and (even more so) brainstem structures. Universal elements were detected in the formation of integral adaptive reactions of the brain with lesions in its regulatory structures, which reflects their close functional interaction and makes it possible to consider them the individual links of an integral regulatory system. The study revealed reciprocal changes in various forms of electrical activity, which reflects reciprocation of interaction of individual regulatory structures. This is one of the EEG equivalents of the formation of adaptive-compensatory cerebral reactions. The specificity of influence of the studied regulatory structures are clearly seen in situations of their morphofunctional isolation observed during cerebral coma. In these conditions, when the cortex is functionally inactive, the authors demonstrated the dynamic character of changes in interhemispheric asymmetry, which reflects the dominance of individual links of the regulatory system playing the role of supreme regulator of life support of the body in critical states.     

Genetic diversity and biocontrol potential of fluorescent pseudomonads producing phloroglucinols and hydrogen cyanide from Swiss soils naturally suppressive or conducive to Thielaviopsis basicola-mediated black root rot of tobacco

Pseudomonas populations producing the biocontrol compounds 2,4-diacetylphloroglucinol (Phl) and hydrogen cyanide (HCN) were found in the rhizosphere of tobacco both in Swiss soils suppressive to Thielaviopsis basicola and in their conducive counterparts. In this study, a collection of Phl+ HCN+Pseudomonas isolates from two suppressive and two conducive soils were used to assess whether suppressiveness could be linked to soil-specific properties of individual pseudomonads. The isolates were compared based on restriction analysis of the biocontrol genes phlD and hcnBC, enterobacterial repetitive intergenic consensus (ERIC)-PCR profiling and their biocontrol ability. Restriction analyses of phlD and hcnBC yielded very concordant relationships between the strains, and suggested significant population differentiation occurring at the soil level, regardless of soil suppressiveness status. This was corroborated by high strain diversity (ERIC-PCR) within each of the four soils and among isolates harboring the same phlD or hcnBC alleles. No correlation was found between the origin of the isolates and their biocontrol activity in vitro and in planta. Significant differences in T. basicola inhibition were however evidenced between the isolates when they were grouped according to their biocontrol alleles. Moreover, two main Pseudomonas lineages differing by the capacity to produce pyoluteorin were evidenced in the collection. Thus, Phl+ HCN+ pseudomonads from suppressive soils were not markedly different from those from nearby conducive soils. Therefore, as far as biocontrol pseudomonads are concerned, this work yields the hypothesis that the suppressiveness of Swiss soils may rely on the differential effects of environmental factors on the expression of key biocontrol genes in pseudomonads rather than differences in population structure of biocontrol Pseudomonas subcommunities or the biocontrol potential of individual Phl+ HCN+ pseudomonad strains.     

Relationship between rapid changes in an individual subrange of the electroencephalogram Δ wave and heart rate during sleep

Correlation analysis was used to determine the relationship between the oscillations of the spectral power of the electroencephalogram (EEG) Δ rhythm subrange corresponding to an individual heart rate and the rhythmocardiogram derivative in healthy subjects during sleep. The EEG range most strongly associated with the individual heart rate of each subject was found. Analysis of temporal shifts showed that changes in the heart rate were several seconds ahead of the corresponding changes in the EEG. The time pattern of the relationship between the studied parameters was significantly correlated with the stages of sleep.     

Correlation of spatial EEG synchronization with verbal-logical and visual-spatial abilities in humans

The EEG was recorded from five monopolar leads: a spontaneous one as well as the EEG during verbal-logical or visual-spatial tests, in humans. The subjects with prevailing visual-spatial abilities had a spontaneous EEG with a low intercorrelation between the hemispheres and a higher level of the EEG synchronisation in parietal-occipital areas as compared with the frontal those, whereas subjects with prevailing verbal abilities had an inverse correlation of the above EEG parameters. The parameters' interrelationships in subjects' spontaneous EEG seem to reflect an individual and specific character of cerebral neurodynamics suggesting a negative correlation between the verbal-logical and visual-spatial abilities in humans.     

Fractal diagnostics of disturbances in the alpha-rhythm dynamics in patients with epilepsy

A new method for analyzing the chaotic component of EEG is proposed. The method is based on estimating the fractal dimension of fluctuations of alpha-rhythm power (the square of amplitude). It is shown that the dimensions of the background EEG fragments for epilepsy patients is significantly higher than that in norm, indicating a disbalance of cerebral mechanisms that control the alpha-activity in this disease. A tendency toward the disturbance of the normal fractal structure of EEG in a group of patients with initial signs of epilepsy was revealed. This suggests that the method is of considerable promise for setting the individual long-term prognosis of the development of the epileptic syndrome.     

Application of recurrence quantification analysis for the automated identification of epileptic EEG signals

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.     

Predicting Epileptic Seizures in Advance

Epilepsy is the second most common neurological disorder, affecting 0.6–0.8% of the world''s population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs) are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredictable nature of seizures poses risks for the individual with epilepsy. It is clearly desirable to find more effective ways of preventing seizures for such patients. The automatic detection of oncoming seizures, before their actual onset, can facilitate timely intervention and hence minimize these risks. In addition, advance prediction of seizures can enrich our understanding of the epileptic brain. In this study, drawing on the body of work behind automatic seizure detection and prediction from digitised Invasive Electroencephalography (EEG) data, a prediction algorithm, ASPPR (Advance Seizure Prediction via Pre-ictal Relabeling), is described. ASPPR facilitates the learning of predictive models targeted at recognizing patterns in EEG activity that are in a specific time window in advance of a seizure. It then exploits advanced machine learning coupled with the design and selection of appropriate features from EEG signals. Results, from evaluating ASPPR independently on 21 different patients, suggest that seizures for many patients can be predicted up to 20 minutes in advance of their onset. Compared to benchmark performance represented by a mean S1-Score (harmonic mean of Sensitivity and Specificity) of 90.6% for predicting seizure onset between 0 and 5 minutes in advance, ASPPR achieves mean S1-Scores of: 96.30% for prediction between 1 and 6 minutes in advance, 96.13% for prediction between 8 and 13 minutes in advance, 94.5% for prediction between 14 and 19 minutes in advance, and 94.2% for prediction between 20 and 25 minutes in advance.     

Relationships between EEG θ and α spectral amplitudes and cognitive ability in preschool children

Relationships between individual θ and α EEG amplitudes and intelligence were studied in five- to six-year-old children. EEG was recorded in 83 children in three functional states: eyes closed, sustained visual attention, and attention to adult’s speech. Intelligence was assessed using Kaufman’s K-ABC test. Relationships between the EEG parameters and intelligence were studied using correlational and regression analyses. The results suggest that stable individual differences in local spectral amplitudes (SA) in the EEG θ and α bands predict reliably (when the covariant proportions of their variances is controlled) the level of simultaneous data processing in children under the conditions of sustained attention but not at rest. Thus, the individual features of the limbic thalamocortical and corticothalamic attentional systems are expected to influence cognitive abilities at the preschool age.     

Individual typological features in the EEG of athletes after acute hypoxic treatment

In athletes with different types of physical training and various temperaments, variability of the main rhythms of the EEG (??, ??, and ??) and the parameters of the desynchronization response of the ??-rhythm, the individual extent of the decrease in the power of the ??-rhythm during eye opening under the conditions of hypoxia growing from 20.9 to 10% of O₂ were studied. Twenty-four 18- to 26-year-old athletes, including 11 swimmers and 13 skiers, were involved in the study. We found that, in contrast to normoxia conditions, hypoxia was associated with the instability of the spectrum of the EEG rhythms and phasic changes during a 25-min hypoxia. The EEG response during hypoxia depended on individual typological features estimated using Strelau??s temperament questionnaire. Negative correlation between the psychological construct ??endurance?? measured using the FCB-TI questionnaire (Formal Characteristics of Behavior-Temperament Inventory) and the power of the EEG ??-rhythm was found. The parameters of training and the features of the respiration pattern that appeared as a consequence of training modulated the sensitivity of brain structures to hypoxia, which was reflected in the changes in the EEG ??-rhythm under hypoxia conditions.     

Individual dynamics of the EEG running wave under various conditions of activity

In 25 healthy subjects at resting with their eyes closed and open, a current phase structure of the EEG was measured, i.e. a trajectory and velocity of oscillation spreading over the head surface in temporal-occipital area. Visual computer multiplication revealed stable individual specifics of the EEG "running wave" dynamics which were then confirmed independently proceeding from the statistical significance criteria. Some subjects had characteristic transversal modulations of the EEG waves (from the left fo the right and vice versa), other subjects manifested longitudinal modulations along the diagonal from the cortex left anterior areas to the right posterior those (in some of them) or along the same diagonal in the opposite direction (in others). When opening the eyes, the character of the EEG "running wave" undergoes dramatic changes. Among other parameters, a growth of the trajectory share occurs in occipital area along with acceleration of the "running wave" spreading on all other trajectories. Both effects are bound to each other and the sharper dynamics is specific for subjects characterised as sympathotonic those as compared with para-sympathotonic.     

Effect of adaptive biocontrol sessions on persons with strong professional skills

We studied the effect of adaptive biocontrol on the quality of the professional activity of an operator possessing stable skills in modeling manual control of station-keeping and berthing to the International Space Station (ISS) of the Soyuz transport ship. The operational quality was evaluated in terms of time, energy, and precision parameters. A computer model of the dynamic ISS image and machinery identical to the real machinery were used in the study.     

EEG and success in learning the job of autotransport driver in persons with different typologic characteristics of the nervous system

The properties of nervous processes: mobility and strength (capacity for work), were determined by the Khilchenko method in healthy subjects learning the trade of an automobile driver. People with different individual typological characteristics of higher nervous activity displayed significant differences in EEG energo-frequency parameters of different brain areas. It has been shown that EEG of practically healthy people at rest, in conjunction with other informative psycho-physiological data, offers promise for appraising the individual typological characteristics of a person in predicting the professional suitability for driving automobiles.     

fMRI-EEG assessment of cerebral reactivity to motor tasks in patients with brain tumor

A comprehensive study with the assessment of reactive responses to motor tasks was performed in nine patients with a tumor localized in the frontal divisions of the brain using two methodological approaches: functional magnetic resonance imaging (fMRI) and EEG. The data obtained were compared to the results of a similar study on 12 healthy subjects. It was established that cerebral pathology was associated with disorders of functional specialization and an increase in the diffuse component of reactivity. The fMRI responses were characterized by greater intactness compared to the EEG parameters of reactive changes. These features are especially marked when an afferent stimulus is sent to the damaged hemisphere. The characteristics of the involvement of individual EEG bands in the formation of motor responses and changes in the fMRI response topography are determined by the degree of cerebral dysfunction reflected by the pattern of baseline EEG reorganization and the severity of the motor defect. The predominant increase in the coherence of slow rhythms in the damaged hemisphere irrespective of the target of the afferent stimulus in patients with severe cerebral dysfunction reflects the dominant formation of a pathological focus and is indicative of a greater, compared to healthy subjects, involvement of deep brain structures in the reactive process, which is confirmed by the fMRI data.