Alexander Ya. Kaplan

Macrostructural EEG characterization based on nonparametric change point segmentation: application to sleep analysis

In the present investigation a new methodology for macrostructural EEG characterization based on automatic segmentation has been applied to sleep analysis. A nonparametric statistical approach for EEG segmentation was chosen, because it minimizes the need for a priori information about a signal. The method provides the detection of change-points i.e. boundaries between quasi-stationary EEG segments based on the EEG characteristics within four fundamental frequency bands (delta, theta, alpha and beta). Polysomnographic data of 18 healthy subjects were analyzed. Our findings show that nonparametric change-point segmentation in combination with cluster analysis enables us to obtain a clear picture of the hierarchical macrostructural organization of sleep, which is impossible to deduce from the unsegmented EEG data. Analysis of correlations between classically defined sleep stages and piecewise stationary power step functions reveals that three basic patterns can be distinguished: SWS (stage III/stage IV), stage II and stage I/REM. In accordance with correlation analyses, cluster detection shows that the cyclic sleep patterns during the course of the night become clearly observable by implementation of only three classes. Since the described methodology is based on a minimum of a priori assumptions, it may be useful for the development of a new sleep classification standard, which goes beyond the established Rechtschaffen and Kales scheme.

Adapting the P300-Based Brain–Computer Interface for Gaming: A Review

The P300-based brain-computer interface (P300 BCI) is currently a very popular topic in assistive technology development. However, only a few simple P300 BCI-based games have been designed so far. Here, we analyze the shortcomings of this BCI in gaming applications and show that solutions for overcoming them already exist, although these techniques are dispersed over several different games. Additionally, new approaches to improve the P300 BCI accuracy and flexibility are currently being proposed in the more general P300 BCI research. The P300 BCI, even in its current form, not only exhibits relatively high speed and accuracy, but also can be used without user training, after a short calibration. Taking these facts together, the broader use of the P300 BCI in BCI-controlled video games is recommended.

UNCONSCIOUS OPERANT CONDITIONING IN THE PARADIGM OF BRAIN-COMPUTER INTERFACE BASED ON COLOR PERCEPTION

This study investigate the mutual fine-tuning of ongoing EEG rhythmic features with RGB values controlling color shades of computer screen during neuro-feedback training. Fifteen participants had not been informed about the existence of neurofeedback loop (NF), but were guided only to look at the computer screen. It was found that during such unconscious NF training, a variety of color shades on the screen gradually changed from rather various types to the main one within the framework of color palette specified for each individual. This phenomenon was not observed in control experiments with simulated neuro-feedback. Individual color patterns induced on the screen during NF did not depend on the schema of connection between of EEG rhythms and RGB controller. It is suggested that the basic neurophysiological mechanism of described NF training consists of the directed selection of EEG patterns reinforced by comfortable color shades without conscious control.

SYSTEMATIC RULES UNDERLYING SPECTRAL PATTERN VARIABILITY: EXPERIMENTAL RESULTS AND A REVIEW OF THE EVIDENCE

On the basis of three different experiments: oddball task (visual, auditory, and audio-visual stimuli), modified Sternbergs, and multistage memory tasks, it was shown that: a) there was not a single typical spectral pattern type that would characterize the majority of the trials; b) the total number of the different spectral pattern types was limited; c) different spectral pattern types had different importance to the brain--their occurrence was more or less probable; d) the total number and the number of the most probable spectral pattern types were dependent on the functional brain state; e) actual spectral pattern of variability during rest with closed eyes was relatively high (around 65% from the maximum possible rate), but significantly less than stochastic spectral pattern variability. It is suggested that identical sensory events can potentially trigger a limited number of several different alternative reaction patterns in EEG/MEG, depending on the situational context.

Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia

We apply flicker-noise spectroscopy (FNS), a time series analysis method operating on structure functions and power spectrum estimates, to study the clinical electroencephalogram (EEG) signals recorded in children/adolescents (11 to 14 years of age) with diagnosed schizophrenia-spectrum symptoms at the National Center for Psychiatric Health (NCPH) of the Russian Academy of Medical Sciences. The EEG signals for these subjects were compared with the signals for a control sample of chronically depressed children/adolescents. The purpose of the study is to look for diagnostic signs of subjects’ susceptibility to schizophrenia in the FNS parameters for specific electrodes and cross-correlations between the signals simultaneously measured at different points on the scalp. Our analysis of EEG signals from scalp-mounted electrodes at locations F3 and F4, which are symmetrically positioned in the left and right frontal areas of cerebral cortex, respectively, demonstrates an essential role of frequency–phase synchronization, a phenomenon representing specific correlations between the characteristic frequencies and phases of excitations in the brain. We introduce quantitative measures of frequency–phase synchronization and systematize the values of FNS parameters for the EEG data. The comparison of our results with the medical diagnoses for 84 subjects performed at NCPH makes it possible to group the EEG signals into 4 categories corresponding to different risk levels of subjects’ susceptibility to schizophrenia. We suggest that the introduced quantitative characteristics and classification of cross-correlations may be used for the diagnosis of schizophrenia at the early stages of its development.

Event-related potentials in a moving matrix modification of the P300 brain-computer interface paradigm

In the standard design of the brain-computer interfaces (BCI) based on the P300 component of the event-related potentials (ERP), target and non-target stimuli are presented at fixed positions in a motionless matrix. Can we let this matrix be moving (e.g., if attached to a robot) without loosing the efficiency of BCI? We assessed changes of the positive peak at Pz in the time interval 300-500 ms after the stimulus onset (P300) and the negative peak at the occipital electrodes in the range 140-240 ms (N1), both important for the operation of the P300 BCI, during fixating a target cell of a moving matrix in healthy participants (n=12). N1 amplitude in the difference (target-non-target) waveforms decreased with the velocity, although remained high (M=-4.3, SD=2.1) even at highest velocity (20°/s). In general, the amplitudes and latencies of these ERP components were remarkably stable in studied types of matrix movement and all velocities of horizontal movement (5, 10 and 20°/s) comparing to matrix in fixed position. These data suggest that, for the users controlling their gaze, the P300 BCI design can be extended to modifications requiring stimuli matrix motion.

Interictal EEG as a physiological adaptation. Part I. Composition of brain oscillations in interictal EEG

OBJECTIVE In the present experimental study, we examined the compositions of brain oscillations and their temporal behavior in broad frequency band (0.5-30 Hz) in interictal EEG without epileptiform abnormalities during generalized epilepsy in resting conditions. METHODS The exact compositions of brain oscillations and their percent ratio were assessed by a probability-classification analysis of short-term EEG spectral patterns (SPs), which reveals temporal dynamics of these SPs and results in the probability-classification profile. RESULTS It has been demonstrated that the interictal EEG was characterized by (a) a shift towards higher frequencies in all observed brain oscillations, (b) an increased amount of polyrhythmic activity, (c) a decrease in SP types diversity, (d) a decreased relative incidence of the SP type change in the transition between neighboring EEG epochs of the same EEG, and (e) an increased temporal stabilization periods of polyrhythmic activity. All these were observed in distributed brain areas. CONCLUSIONS It was suggested that these findings reflect a disorganization of neurodynamics in the epileptic brain. At the same time, the fact that all these indices were significantly different from surrogate EEG reflects a non-occasional and thus, most likely, an adaptive nature of the microstructural reorganization of interictal EEG. SIGNIFICANCE Parameters of interictal EEG without the signs of epileptiform activity can be considered as additional information in premorbid diagnostics of epistatus.

A P300-based brain-computer interface with stimuli on moving objects: four-session single-trial and triple-trial tests with a game-like task design.

Brain-computer interfaces (BCIs) are tools for controlling computers and other devices without using muscular activity, employing user-controlled variations in signals recorded from the user’s brain. One of the most efficient noninvasive BCIs is based on the P300 wave of the brain’s response to stimuli and is therefore referred to as the P300 BCI. Many modifications of this BCI have been proposed to further improve the BCI’s characteristics or to better adapt the BCI to various applications. However, in the original P300 BCI and in all of its modifications, the spatial positions of stimuli were fixed relative to each other, which can impose constraints on designing applications controlled by this BCI. We designed and tested a P300 BCI with stimuli presented on objects that were freely moving on a screen at a speed of 5.4°/s. Healthy participants practiced a game-like task with this BCI in either single-trial or triple-trial mode within four sessions. At each step, the participants were required to select one of nine moving objects. The mean online accuracy of BCI-based selection was 81% in the triple-trial mode and 65% in the single-trial mode. A relatively high P300 amplitude was observed in response to targets in most participants. Self-rated interest in the task was high and stable over the four sessions (the medians in the 1st/4th sessions were 79/84% and 76/71% in the groups practicing in the single-trial and triple-trial modes, respectively). We conclude that the movement of stimulus positions relative to each other may not prevent the efficient use of the P300 BCI by people controlling their gaze, e.g., in robotic devices and in video games.

Interictal EEG as a physiological adaptation. Part II. Topographic variability of composition of brain oscillations in interictal EEG

OBJECTIVE In the present experimental study, we examined topographic variability of composition of brain oscillations and their temporal behavior in frequencies from 0.5 to 30 Hz of interictal EEG without epileptiform abnormalities and healthy EEG. METHODS Spatio-temporal variability of brain oscillations (indexed by short-term EEG spectral patterns (SPs)) was assessed by the probability-classification analysis of SPs. As a result, multi-dimensional SP-vector for each analysis EEG epoch was obtained. RESULTS It was demonstrated that interictal EEG was characterized (a) by a significant decrease of spatio-temporal variability of brain oscillations, (b) by longer periods of temporal stabilization for operational modules which comprise larger number of cortical areas, and (c) by significantly more intermittent recurrence when compared with EEG of control subjects. Generally it was shown that EEG channels display different states of coordination independently on their correlation and coherence using brain oscillations at multiple frequencies. CONCLUSIONS Results of this study suggested that EEG correlate of chronic epileptogenesis in the brain is a particular metastable state of biopotential field, which can be estimated by SP-vector. The fact that all results were significantly different from surrogate EEGs reflects a nonoccasional and thus, most likely, an adaptive nature of spatio-temporal reorganization in interictal EEG. SIGNIFICANCE Parameters of spatio-temporal organization of interictal EEG without the signs of epileptiform activity can be considered as additional information in premorbid diagnostics of status epilepticus, and may also provide insights into basic laws that govern brain oscillations in general.

A model for the speed of memory retrieval

Abstract.The memory retrieval process of number problems with external noise is studied with the use of the Bonhoeffer–van der Pol oscillator model. Three cell assembly responses are simulated, coding one true number and two neighboring erroneous. The time of a correct response, Tc, was averaged over statistical assemblies of numerous trials. It is demonstrated that Tc takes a minimum value for a certain noise intensity. This result correlates well with experimental data by Usher and Feingold (2000). The location of the minimum as a function of the time delay between two consecutive simulation trials is investigated.