SummaryThis review focuses on the published literature on vestibular disorders following different types of head and neck trauma. Current knowledge of the different causes and underlying mechanisms of vestibular disorders, as well as the sites of organic damage, is presented. Non-organic mechanisms are also surveyed. The frequency of occurrence of vestibular symptoms, and of other accompanying subjective complaints, associated with different types of trauma is presented and related to the specific causes. Hypotheses about the pathogenesis of traumatic vestibular disorders are presented, and the knowledge derived from animal experiments is also discussed. We believe this to be a very important topic, since vestibular complaints in traumatic patients often remain undiagnosed or underestimated in clinical practice. This review article aims to suggest directions for additional research and to provide guidance to both the scientific and clinical practice communities.

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The article discusses well-known and ongoing studies of mechanisms of action of melatonin. The main clinical effects of melatonin are discussed. The emphasis on the chronobiological effect of melatonin, its adaptogenic and anti-carcinogenic properties has been done in the article. The most frequent manifestations of epiphyseal melatonin deficiency are various functional disorders in the form of insomnia, anxiety or depressive disorders. Recommendations on the effective use of melatonin in its deficiency due to pathology are given.

Aim - developing the integral algorithm of recognition of the evoked potential (ERP-response) to a target visual stimulus and testing of the proposed algorithm on the wireless 5-channel electroencephalograph Emotiv Insight with “dry” electrodes. Materials and methods. The objects of the study were the EEG records of five volunteers. Were used 5-channel wireless EEG headset Emotiv Insight, self-developed software «eSpeller», software environment MathWork® MATLAB R2015a. Results. It was found that the proposed integral algorithm of recognition of electrical activity of the cerebral cortex to a target visual stimulus shows the accuracy of the detection from 71.5% to 90.6% with the average value 80.1+7.2%, using EEG headset Emotiv Insight. Conclusion. The algorithm shows a high level of reliability of recognition of evoked potential to a target visual stimulus, does not require large computing power, sophisticated classification methods and machine learning. The testing of the algorithm suggests the possibility of using the electroencephalograph Emotiv Insight with "dry" electrodes in the development of BCI.