Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.

Decoding Individual Finger Movements from One Hand Using Human EEG Signals

Conventional mass-univariate analyses have been previously used to test for group differences in neural signals. However, machine learning algorithms represent a multivariate decoding approach that may help to identify neuroimaging patterns associated with functional impairment in "individual" patients. We investigated whether fMRI allows classification of individual motor impairment after stroke using support vector machines (SVMs). Forty acute stroke patients and 20 control subjects underwent resting-state fMRI. Half of the patients showed significant impairment in hand motor function. Resting-state connectivity was computed by means of whole-brain correlations of seed time-courses in ipsilesional primary motor cortex (M1). Lesion location was identified using diffusion-weighted images. These features were used for linear SVM classification of unseen patients with respect to motor impairment. SVM results were compared with conventional mass-univariate analyses. Resting-state connectivity classified patients with hand motor deficits compared with controls and nonimpaired patients with 82.6-87.6% accuracy. Classification was driven by reduced interhemispheric M1 connectivity and enhanced connectivity between ipsilesional M1 and premotor areas. In contrast, lesion location provided only 50% sensitivity to classify impaired patients. Hence, resting-state fMRI reflects behavioral deficits more accurately than structural MRI. In conclusion, multivariate fMRI analyses offer the potential to serve as markers for endophenotypes of functional impairment.

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Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques

Objective Movement control is an important application for EEG-BCI (EEG-based brain-computer interface) systems. A single-modality BCI cannot provide an efficient and natural control strategy, but a hybrid BCI system that combines two or more different tasks can effectively overcome the drawbacks encountered in single-modality BCI control. Approach In the current paper, we developed a new hybrid BCI system by combining MI (motor imagery) and mVEP (motion-onset visual evoked potential), aiming to realize the more efficient 2D movement control of a cursor. Main result The offline analysis demonstrates that the hybrid BCI system proposed in this paper could evoke the desired MI and mVEP signal features simultaneously, and both are very close to those evoked in the single-modality BCI task. Furthermore, the online 2D movement control experiment reveals that the proposed hybrid BCI system could provide more efficient and natural control commands. Significance The proposed hybrid BCI system is compensative to realize efficient 2D movement control for a practical online system, especially for those situations in which P300 stimuli are not suitable to be applied.

https://iopscience.iop.org/article/10.1088/1741-2552/aa5d5f/pdf

The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential.

Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface

Electroencephalography (EEG) signal processing to decode motor imagery (MI) involves high-dimensional features, which increases the computational complexity. To reduce this computational burden due to the large number of channels, an iterative multiobjective optimization for channel selection (IMOCS) is proposed in this paper. For a given MI classification task, the proposed method initializes a reference candidate solution and subsequently finds a set of the most relevant channels in an iterative manner by exploiting both the anatomical and functional relevance of EEG channels. The proposed approach is evaluated on the Wadsworth dataset for the right fist versus left fist MI tasks, while considering the cross-validation accuracy as the performance evaluation criteria. Furthermore, 12 other dimension reduction and channel selection algorithms are used for benchmarking. The proposed approach (IMOCS) achieved an average classification accuracy of about 80% when evaluated using 35 best-performing subjects. One-way analysis of variance revealed the statistical significance of the proposed approach with at least 7% improvement over other benchmarking algorithms. Furthermore, a cross-subject generalization of channel selection on untrained subjects shows that the subject-independent channels perform as good as using all channels achieving an average classification accuracy of 61%. These results are promising for the online brain–computer interface (BCI) paradigm that requires low computational complexity and also for reducing the preparation time while conducting multiple session BCI experiments for a larger pool of subjects.

Optimized Bi-Objective EEG Channel Selection and Cross-Subject Generalization With Brain–Computer Interfaces

Transcranial electrostimulation of hemodynamics of the liver, systemic microcirculation, severity of cytolytic syndrome, lipid peroxidation processes, and condition of enzyme antioxidant defense in patients with chronic diffuse liver diseases (CDLD) as a method of drug-free treatment of such patients is discussed.

Transcranial Electrostimulation in Treatment of Chronic Diffuse Liver Diseases

Тромбоз воротной вены (ТВВ) это форма венозного тромбоза, вызывающая нарушение или прекращение кровотока в воротной вене. ТВВ обусловлен рядом этиологических факторов, которые разделяют на две группы: локальные и системные. ТВВ при циррозе печени сопровождается увеличением риска как геморрагических, так и тромботических осложнений. ТВВ классифицируется по течению, этиологии, наличию окклюзии и осложнениям. В диагностике данного состояния важную роль занимают УЗИ, КТ и МРТ. К методам лечения ТВВ относятся тщательное наблюдение без вмешательства, терапия антикоагулянтами, тромболизис, тромбэктомия, ангиопластика и трансъюгулярный внутрипеченочный портосистемный шунт. Согласно клиническим рекомендациям EASL по лечению заболеваний сосудов печени рекомендуется назначение антикоагулянтов прямого действия. Ривaроксабан, а также другие прямые пероральные антикоагулянты являются перспективными препаратами для лечения ТВВ, так как обладают рядом преимуществ.

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Portal vein thrombosis in the practice of the internist

Nephrotic syndrome in the work of a family doctor

Cholelithiasis in general medicine practice

Vascular stiffness is an independent prognostic parameter for the development of cardiac pathology and cardiovascular mortality. The predictive value is high at the preclinical stages of the development of the disease, since blood vessels are an integral marker of cardiovascular pathology, through which classic risk factors for cardiovascular diseases (CVD) are realized. Vascular stiffness increases due to cholesterol deposits and calcification of the vessel wall. On the other hand, an increase in blood pressure (BP) causes changes in the vascular wall, including with the development of arteriolosclerosis, which can also lead to an increase in vascular stiffness.

Vsevolod Skvortsov

Papers

Transcranial Electrostimulation in Treatment of Chronic Diffuse Liver Diseases

Portal vein thrombosis in the practice of the internist

Nephrotic syndrome in the work of a family doctor

Cholelithiasis in general medicine practice

Modern methods for determining and risk factors for the development of arterial stiffness in the practice of family medicine