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Cross-frequency coupling (CFC) is typical for the operation of neural networks from different areas of the brain. This, for example, can be characterized by “рacemaker” neurons activity, the structure of these part’s crust, etc. Thus, the highest interest is not the correlation of those areas, but the synchronous activity of the areas in time at different frequencies. Event-related events can induce the work of neurons, but each at its own frequency. It looks like a synchronous manifestation of activity at different points in time, with a lag, but appearing at different frequencies. Correlation methods and coherence for CFC detection are not suitable, since they are for monofrequencies, and a long time series is required. The connectedness of neurons ensemble’s work in time is effectively considered by methods such as bispectrum and CTDC. In this work, we compared these two methods as well as their hybrid. According to the results, the CTDC method proved to be more accurate, both in spatial localization and in inter-frequency.

Brain-Computer Interface (BCI) is a system enabling direct communication between the brain and external devices. It has large potential in neurorehabilitation. New optically-pumped magnetometer (OPM)-based recording systems have large potential among noninvasive electroencephalographic and traditional magnetoencephalographic BCI-systems., that should be revealed., however. Here we demonstrated good performance of the hand-movement imagery BCI., built on low number of OPM-sensors.

The final aim of our research is to develop a braincomputer interface (BCI) for olfaction. Our research program relies on modern olfactory displays and advanced processing of electroencephalography (EEG) and respiratory data in order to develop methods for robust olfactory BCI systems. Here we present the initial results from 17 subjects of our ongoing study. Applying k-nearest neighbors algorithm (k-NN) classification methods we achieve up to 79.9% accuracy for within subject classification of EEG signals for odor pairs. We propose some methods for further improvement of classification algorithm. EEG classification for different olfactory stimuli is the first step in this research, followed by the development of odor-imagery BCIs and odor-based neurofeedback.

One of the main methodological problems in evaluation of functional connectivity is the spatial leakage (SL) effect which occurs due to volume conduction and leads to false positives in coherence or phase-locking estimates. Several solutions have been already suggested, including the use of the imaginary part of coherency or cross-spectrum. Because these standard metrics are insensitive to zero-phase interactions, they prevent detection of false coupling, resulting from SL, but may underestimate true physiological interactions, characterized by close-to-zero phase lags. Due to the broad neurophysiological evidence, such interactions should not be excluded from consideration. The recently proposed method, referred as Phase Shift Invariant Imaging of Coherent Sources (PSIICOS), became the first implementation of the algorithm which reliably detects interactions for all the range of phase-lags by suppressing the power of SL subspace components of cross-spectrum. However, connectivity values obtained via PSIICOS are non-normalized by construction and depend on source power, so that uncoupled sources with high power profiles may become false positives. This limitation motivated us to develop a statistical test based on randomization of original time series or cross-spectrum in such a way that power distribution in source space is preserved, but phase interactions are eliminated. The generation of covariance matrices from Wishart distribution appeared to be the most reliable method, when applied to data from simulations. Thus, together with the proposed statistical test PSIICOS can be used as an effective instrument applicable to real EEG- or MEG-data in fundamental research or for clinical purposes.

In this work, we motivate and present a novel compact CNN. For the architectures that combine the adaptation in both space and time, we describen a theoretically justified approach to interpreting the temporal and spatial weights. We apply the proposed architecture to Berlin BCI IV competition and our own datasets to decode electrocorticogram into finger kinematics. Without feature engineering our architecture delivers similar or better decoding accuracy as compared to the BCI competition winner. After training the network, we interpret the solution (spatial and temporal convolution weights) and extract physiologically meaningful patterns.

We present a solution to employ brain-controlled biometric signals to operate external technical devices. Such signals include multiple electrode electroencephalographic (EEG) signals, electromyography (EMG) signals reflecting muscle contraction pattern, geometrical pattern of body limb kinematics, and other modalities. Being collected, properly decoded and interpreted, the signals can be used as a control or navigation signals of artificial machines, e.g., technical devices. Our interface solution based on a combination of signals of different modalities is capable to provide composite command and proportional multisite control of different technical devices with theoretically unlimited computational power. The feedback to the operator by visual channel or in virtual reality permits to compensate control errors and provides adaptive control. The control system can be implemented with wearable electronics. Examples of technical devices under the control are presented.

A method to construct a feature space for electroencephalogram (EEG) classification based on the localization of brain’s electrical activity sources is developed.The purpose of the work is to show that a model-based approach to the construction of feature space for EEG classification allows us to achieve the accuracy comparable to existing classical approaches at the same time giving a number of opportunities to further increase it and having clear neurophysiological interpretation.Experimental researches on real EEG show that the accuracy of the proposed method is comparable to the accuracy of the classical method of classification in brain-computer interfaces. The simplest statistical characteristics of dipole moments for equivalent current dipoles are chosen as features for classification, and the nearest neighbour algorithm is used for classification.Application of the proposed algorithm is diagnostics of brain diseases and braincomputer interfaces.The first section describes a method of modeling the EEG using equivalent current dipoles.In the second section the statement of the EEG classification problem is formulated.In the third section we propose a method of constructing a feature space for EEG classification based on the equivalent current dipoles characteristics.The fourth section is dedicated to the experimental research of the proposed method on real EEG and to discussion of the results achieved.

Humans often change their beliefs or behavior due to the behavior or opinions of others. This study explored with the use of human event related potentials (ERPs) whether social conformity is based on a general performance monitoring mechanism. We tested the hypothesis that conflicts with a normative group opinion evoke a feedback related negativity (FRN) often associated with performance monitoring and subsequent adjustment of behavior. The experimental results show that individual judgments of facial attractiveness were adjusted in line with a normative group opinion. A mismatch between individual and group opinions triggered a frontocentral negative deflection with the maximum at 200 ms similar to FRN. Overall a conflict with a normative group opinion triggered a cascade of neuronal responses: from an earlier FRN response reflecting a conflict with the normative opinion to a later ERP component (peaking at 380 ms) reflecting a conforming behavioral adjustment. These results add to the growing literature on neuronal mechanisms of social influence by disentangling the conflict monitoring signal in response to the perceived violation of social norms and the neural signal of a conforming behavioral adjustment. © The Author (2012). Published by Oxford University Press.