Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …

The paper is devoted to the review of the coherence resonance phenomenon in excitable
neural networks. In particular, we explain how coherence can be measured and how noise …

The human brain has the capacity to rapidly change state, and in epilepsy these state
changes can be catastrophic, resulting in loss of consciousness, injury and even death …

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 …

In this paper we study a chimera state in complex networks of bistable Hodgkin-Huxley
neurons with excitatory coupling, which manifests as a termination of spiking activity of a part …

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 …

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are
used in social science, robotics, and neurophysiology for solving tasks of classification …

Brain-computer interfaces (BCIs) attract a lot of attention because of their ability to improve
the brain's efficiency in performing complex tasks using a computer. Furthermore, BCIs can …

We propose a theoretical approach associated with an experimental technique to
quantitatively characterize cognitive brain activity in the perception of ambiguous images …

We consider a neuronal network model where an external stimulus excites some neurons,
which in turn activate other neurons in the network via synapse. We find that the regularity in …