Non-invasive EEG-based BCI spellers from the beginning to today: a mini-review

The defeat of the central motor neuron leads to the motor disorders. Patients lose the ability to control voluntary muscles, for example, of the upper limbs, which introduces a fundamental dissonance in the possibility of daily use of a computer or smartphone. As a result, the patients lose the ability to communicate with other people. The article presents the most popular paradigms used in the brain-computer-interface speller system and designed for typing by people with severe forms of the movement disorders. Brain-computer interfaces (BCIs) have emerged as a promising technology for individuals with communication impairments. BCI-spellers are systems that enable users to spell words by selecting letters on a computer screen using their brain activity. There are three main types of BCI-spellers: P300, motor imagery (MI), and steady-state visual evoked potential (SSVEP). However, each type has its own limitations, which has led to the development of hybrid BCI-spellers that combine the strengths of multiple types. Hybrid BCI-spellers can improve accuracy and reduce the training period required for users to become proficient. Overall, hybrid BCI-spellers have the potential to improve communication for individuals with impairments by combining the strengths of multiple types of BCI-spellers. In conclusion, BCI-spellers are a promising technology for individuals with communication impairments. P300, MI, and SSVEP are the three main types of BCI-spellers, each with their own advantages and limitations. Further research is needed to improve the accuracy and usability of BCI-spellers and to explore their potential applications in other areas such as gaming and virtual reality

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network

Large-scale functional connectivity is an important indicator of the brain’s normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the functional connectivity assessment based on artificial intelligence to reveal age-related changes in neural response in a simple motor execution task. Twenty subjects of two age groups performed repetitive motor tasks on command, while the whole-scalp EEG was recorded. We applied the model based on the feed-forward multilayer perceptron to detect functional relationships between five groups of sensors located over the frontal, parietal, left, right, and middle motor cortex. Functional dependence was evaluated with the predicted and original time series coefficient of determination. Then, we applied statistical analysis to highlight the significant features of the functional connectivity network assessed by our model. Our findings revealed the connectivity pattern is consistent with modern ideas of the healthy aging effect on neural activation. Elderly adults demonstrate a pronounced activation of the whole-brain theta-band network and decreased activation of frontal–parietal and motor areas of the mu-band. Between-subject analysis revealed a strengthening of inter-areal task-relevant links in elderly adults. These findings can be interpreted as an increased cognitive demand in elderly adults to perform simple motor tasks with the dominant hand, induced by age-related working memory decline

Explainable Machine Learning Methods for Classification of Brain States during Visual Perception

The aim of this work is to find a good mathematical model for the classification of brain states during visual perception with a focus on the interpretability of the results. To achieve it, we use the deep learning models with different activation functions and optimization methods for their comparison and find the best model for the considered dataset of 31 EEG channels trials. To estimate the influence of different features on the classification process and make the method more interpretable, we use the SHAP library technique. We find that the best optimization method is Adagrad and the worst one is FTRL. In addition, we find that only Adagrad works well for both linear and tangent models. The results could be useful for EEG-based brain–computer interfaces (BCIs) in part for choosing the appropriate machine learning methods and features for the correct training of the BCI intelligent system

Silicon dioxide thin film mediated single cell nucleic acid isolation.

A limited amount of DNA extracted from single cells, and the development of single cell diagnostics make it necessary to create a new highly effective method for the single cells nucleic acids isolation. In this paper, we propose the DNA isolation method from biomaterials with limited DNA quantity in sample, and from samples with degradable DNA based on the use of solid-phase adsorbent silicon dioxide nanofilm deposited on the inner surface of PCR tube

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented

Formation of Media Competence of Future Teachers by Means of ICT and Mobile Technologies

The paper is devoted to the problem of formation of media competence of future teachers by means of ICT and mobile technologies. It discusses the concepts of media competence, media education and media culture. Motivational, cognitive, technological, reflective components within the structure of media competence are revealed in the article. The results of experimental work of formation of media competence of future teachers by means of ICT and mobile learning technologies are presented. The authors conclude that study of modern methodological and technological methods of media education, history of their emergence and development, creative use within the educational process opens new perspectives for the application of the educational and developmental potential of media education at school and universities

SDTF chemical structure and plastic (polypropylene) sample with functional covering scheme.

<p>A) The result of ion etching of thin films layers of Ag(indicator)/SiO₂(silicon dioxide)/C(polypropylene)-substrate by Auger electron spectroscopy thin films ion profiling PCR tube inner surface sample. The spectrum indicates the chemical elements surface concentration by results of which is observed the absence of silicon dioxide on the PCR tube inner surface. B) Ag(indicator)/SiO₂(silicon dioxide)/C(polypropylene)-substrate surface concentration on inner surface of PCR tube after AES Ion profiling of thin films. The spectrum indicates an increase of the silicon and oxygen peaks. C) Auger electron spectroscopy depth profile of SDTF on graphite substrate. D) The dashes line show the research area of PCR tube inner surface.</p

Phase analysis, element distribution mapping of plastic (polypropylene) sample with functional covering and real-time polymerase chain reaction.

<p>A) Raman spectrum of two samples of inner surface PCR tubes with amorphous SDTF prepared by the IBD method under the same conditions. B) Auger electron spectroscopy element distribution mapping. Scale bar is 200 µm. The spectrum color scheme shows distribution of: carbon (C) – upper left scan sector, oxygen (O) – upper right scan sector, silicon (Si) – lower left scan sector and secondary electron image (lower right scan sector). C) Amplification curves obtained using 1– approach, which involves the introduction of a single cell (oocyte) in a tube without silicon dioxide nanocovering; 2– method implied the introduction of a single cell (oocyte) in a tube with silicon dioxide nanocovering, followed by a cell lysis and washing of tubes from impurities. It gave uniform positive results in the course of PCR, which confirms the efficient extraction of DNA from single cells.</p

The Oligomeric Form of the Escherichia coli Dps Protein Depends on the Availability of Iron Ions

The Dps protein of Escherichia coli, which combines ferroxidase activity and the ability to bind DNA, is effectively used by bacteria to protect their genomes from damage. Both activities depend on the integrity of this multi-subunit protein, which has an inner cavity for iron oxides; however, the diversity of its oligomeric forms has only been studied fragmentarily. Here, we show that iron ions stabilize the dodecameric form of Dps. This was found by electrophoretic fractionation and size exclusion chromatography, which revealed several oligomers in highly purified protein samples and demonstrated their conversion to dodecamers in the presence of 1 mM Mohr’s salt. The transmission electron microscopy data contradicted the assumption that the stabilizing effect is given by the optimal core size formed in the inner cavity of Dps. The charge state of iron ions was evaluated using Mössbauer spectroscopy, which showed the presence of Fe3O4, rather than the expected Fe2O3, in the sample. Assuming that Fe2+ can form additional inter-subunit contacts, we modeled the interaction of FeO and Fe2O3 with Dps, but the binding sites with putative functionality were predicted only for Fe2O3. The question of how the dodecameric form can be stabilized by ferric oxides is discussed