We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10‐10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time‐frequency structure of EEG signals and find that motor‐related features associated with left and right leg motor imagery are more pronounced in the mu (8–13 Hz) and delta (1–5 Hz) brainwaves than in the high‐frequency beta brainwave (15–30 Hz). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low‐pass filter with different cutoffs. We demonstrate that the filtration of high‐frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain‐computer interfaces for untrained subjects.

In this paper we study the structural properties of a functional network of the human brain during the evaluation of mental tasks using the concept of betweenness centrality. We carry out the experiments involving the alternating trials of mental task evaluation with simultaneous registration of electroencephalographic (EEG) data. Using the wavelet phase coherence we reconstruct the functional multiplex network of the brain considering the different typical frequency bands of EEG activity as interconnected layers. We reveal that transition from a resting state to evaluation of a cognitive task leads to the strong outflow of shortest paths from low frequencies and strengthening of high-frequency connectivity in the brain. At the same time, we observe that mental activity shapes the shortest paths in a more uniform distribution across the brain, which implies the emergence of a more distributed functional network. Our results are in good agreement with recent studies of cognitive activity and can be implied in the design of brain-computer interfaces for the estimation of cognitive load or attention.

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.

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 increase human's performance not only due to human-machine interactions, but also thanks to an optimal distribution of cognitive load among all members of a group working on a common task, i.e., due to human-human interaction. The latter is of particular importance when sustained attention and alertness are required. In every day practice, this is a common occurrence, for example, among office workers, pilots of a military or a civil aircraft, power plant operators, etc. Their routinely work includes continuous monitoring of instrument readings and implies a heavy cognitive load due to processing large amounts of visual information. In this paper, we propose a brain-to-brain interface (BBI) which estimates brain states of every participant and distributes a cognitive load among all members of the group accomplishing together a common task. The BBI allows sharing the whole workload between all participants depending on their current cognitive performance estimated from their electrical brain activity. We show that the team efficiency can be increased due to redistribution of the work between participants so that the most difficult workload falls on the operator who exhibits maximum performance. Finally, we demonstrate that the human-to-human interaction is more efficient in the presence of a certain delay determined by brain rhythms. The obtained results are promising for the development of a new generation of communication systems based on neurophysiological brain activity of interacting people. Such BBIs will distribute a common task between all group members according to their individual physical conditions.