Brain-computer interface (BCI) provides direct communication between the brain and an external device. BCI systems have become a trendy field of research in recent years. These systems can be used in a variety of applications to help both disabled and healthy people. Concerning significant BCI progress, we may assume that these systems are not very far from real-world applications. This review has taken into account current trends in BCI research. In this survey, 100 most cited articles from the WOS database were selected over the last 4 years. This survey is divided into several sectors. These sectors are Medicine, Communication and Control, Entertainment, and Other BCI applications. The application area, recording method, signal acquisition types, and countries of origin have been identified in each article. This survey provides an overview of the BCI articles published from 2016 to 2020 and their current trends and advances in different application areas.

• Klíčová slova
• BCI applications, BCI entertainment, BCI in medicine, brain-computer interface (BCI), communication and control in BCI, invasive BCI, noninvasive BCI,
• MeSH
• elektroencefalografie MeSH
• lidé MeSH
• mozek MeSH
• pomůcky pro komunikaci postižených * MeSH
• rozhraní mozek-počítač * MeSH
• uživatelské rozhraní počítače MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Objective.Functional specialization is fundamental to neural information processing. Here, we study whether and how functional specialization emerges in artificial deep convolutional neural networks (CNNs) during a brain-computer interfacing (BCI) task.Approach.We trained CNNs to predict hand movement speed from intracranial electroencephalography (iEEG) and delineated how units across the different CNN hidden layers learned to represent the iEEG signal.Main results.We show that distinct, functionally interpretable neural populations emerged as a result of the training process. While some units became sensitive to either iEEG amplitude or phase, others showed bimodal behavior with significant sensitivity to both features. Pruning of highly sensitive units resulted in a steep drop of decoding accuracy not observed for pruning of less sensitive units, highlighting the functional relevance of the amplitude- and phase-specialized populations.Significance.We anticipate that emergent functional specialization as uncovered here will become a key concept in research towards interpretable deep learning for neuroscience and BCI applications.

• Klíčová slova
• brain–computer interface (BCI), deep learning, explainable AI (XAI), internal representation, intracranial EEG (iEEG), motor decoding, neural network visualization,
• MeSH
• algoritmy MeSH
• elektroencefalografie metody   MeSH
• mozek MeSH
• neuronové sítě (počítačové) MeSH
• rozhraní mozek-počítač * MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.

• Klíčová slova
• EEG, electroencephalography, evolutionary algorithms, nature-inspired metaheuristics, optimization,
• MeSH
• algoritmy * MeSH
• artefakty MeSH
• elektroencefalografie * metody   MeSH
• lidé MeSH
• mozek * fyziologie   MeSH
• rozhraní mozek-počítač MeSH
• strojové učení MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Guess the number is a simple P300-based brain-computer interface experiment. Its aim is to ask the measured participant to pick a number between 1 and 9. Then, he or she is exposed to corresponding visual stimuli and experimenters try to guess the number thought while they are observing event-related potential waveforms on-line. 250 school-age children participated in the experiments that were carried out in elementary and secondary schools in the Czech Republic. Electroencephalographic data from three EEG channels (Fz, Cz, Pz) and stimuli markers were stored. Additional metadata about the participants were collected (gender, age, laterality, the number thought by the participant, the guess of the experimenters, and various interesting additional information). Consequently, we offer the largest publicly available odd-ball paradigm collection of datasets to neuroscientific and brain-computer interface community.

• MeSH
• dítě MeSH
• elektroencefalografie MeSH
• lidé MeSH
• mladiství MeSH
• rozhraní mozek-počítač * MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika MeSH

BACKGROUND: Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. OBJECTIVES: Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. METHODS: Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination. RESULTS: The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. CONCLUSIONS: Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration.

• Klíčová slova
• Artifacts, Brain computer interface, Deep brain stimulation, Neuromodulation, Oscillations,
• MeSH
• algoritmy MeSH
• artefakty MeSH
• elektrokardiografie MeSH
• esenciální tremor * MeSH
• lidé MeSH
• rozhraní mozek-počítač * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Depression is a major depressive disorder characterized by persistent sadness and a sense of worthlessness, as well as a loss of interest in pleasurable activities, which leads to a variety of physical and emotional problems. It is a worldwide illness that affects millions of people and should be detected at an early stage to prevent negative effects on an individual's life. Electroencephalogram (EEG) is a non-invasive technique for detecting depression that analyses brain signals to determine the current mental state of depressed subjects. In this study, we propose a method for automatic feature extraction to detect depression by first constructing a graph from the dataset where the nodes represent the subjects in the dataset and where the edge weights obtained using the Euclidean distance reflect the relationship between them. The Node2vec algorithmic framework is then used to compute feature representations for nodes in a graph in the form of node embeddings ensuring that similar nodes in the graph remain near in the embedding. These node embeddings act as useful features which can be directly used by classification algorithms to determine whether a subject is depressed thus reducing the effort required for manual handcrafted feature extraction. To combine the features collected from the multiple channels of the EEG data, the method proposes three types of fusion methods: graph-level fusion, feature-level fusion, and decision-level fusion. The proposed method is tested on three publicly available datasets with 3, 20, and 128 channels, respectively, and compared to five state-of-the-art methods. The results show that the proposed method detects depression effectively with a peak accuracy of 0.933 in decision-level fusion, which is the highest among the state-of-the-art methods.

• Klíčová slova
• Decision-level fusion, Electroencephalography, Feature-level fusion, Graph-level fusion, Node2vec,
• MeSH
• algoritmy MeSH
• deprese diagnóza   MeSH
• depresivní porucha unipolární * diagnóza   MeSH
• elektroencefalografie MeSH
• lidé MeSH
• rozhraní mozek-počítač * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

For an algebra [Formula: see text] belonging to a quasivariety [Formula: see text], the quotient [Formula: see text] need not belong to [Formula: see text] for every [Formula: see text]. The natural question arises for which [Formula: see text]. We consider algebras [Formula: see text] of type (2, 0) where a partial order relation is determined by the operations [Formula: see text] and 1. Within these, we characterize congruences on [Formula: see text] for which [Formula: see text] belongs to the same quasivariety as [Formula: see text]. In several particular cases, these congruences are determined by the property that every class is a convex subset of A.

• Klíčová slova
• Algebra with induced order, BCI-algebra, BCK-algebra, Convex class, Convex congruence,
• Publikační typ
• časopisecké články MeSH

Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.

• Klíčová slova
• Artificial intelligence, Artificial neural networks, Brain–computer interfaces, Fuzzy logic, Machine learning, Nature-inspired optimization techniques,
• MeSH
• algoritmy MeSH
• kvalita života MeSH
• lidé MeSH
• mozek MeSH
• počítače MeSH
• rozhraní mozek-počítač * MeSH
• strojové učení MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH

This paper presents a gamified motor imagery brain-computer interface (MI-BCI) training in immersive virtual reality. The aim of the proposed training method is to increase engagement, attention, and motivation in co-adaptive event-driven MI-BCI training. This was achieved using gamification, progressive increase of the training pace, and virtual reality design reinforcing body ownership transfer (embodiment) into the avatar. From the 20 healthy participants performing 6 runs of 2-class MI-BCI training (left/right hand), 19 were trained for a basic level of MI-BCI operation, with average peak accuracy in the session = 75.84%. This confirms the proposed training method succeeded in improvement of the MI-BCI skills; moreover, participants were leaving the session in high positive affect. Although the performance was not directly correlated to the degree of embodiment, subjective magnitude of the body ownership transfer illusion correlated with the ability to modulate the sensorimotor rhythm.

• Klíčová slova
• body ownership transfer, brain-computer interface, embodiment, gamification, motor imagery,
• Publikační typ
• časopisecké články MeSH

Leptospirosis is a widespread infection among pigs throughout the world. In most cases in Ukraine, only the microscopic agglutination test (MAT) is used for the diagnosis of leptospirosis in animals. In general, during the period of 2001-2019, 2 381 163 samples of blood sera from swine were tested in our country and 85 338 positive reactions were received, which is 3.58% [binomial confidence intervals (BCI), 3.56-3.61%]. It was established that the serovars copenhageni - 33.91% (BCI, 33.59-34.23%), bratislava - 14.14% (BCI, 13.90-14.37%), pomona - 8.58% (BCI, 8.39-8.77%), and tarassovi - 7.12% (BCI, 6.95-7.30%) play a leading role in the aetiological structure of swine leptospirosis. A large number of positive reactions to several serovars was observed - 29.78% (BCI, 29.47-30.09%) of the total number of positive cases. In addition, the article presents data according to a retrospective analysis of the eight serovars circulating among pigs in Ukraine. Thus, during the nineteen year period, there was a decrease in the number of positive reactions to bratislava, pomona, and tarassovi and an increase in the number of positive reactions to copenhageni, polonica, and kabura. Mapping Ukraine's territory for leptospirosis among pigs was carried out. This allows one to identify zones with a risk of leptospirosis infections among swine. The maps show that the highest incidence rates were identified in the eastern and central parts of Ukraine.

• Klíčová slova
• GIS, etiological structure, leptospirosis, mapping, microscopic agglutination test, swine,
• Publikační typ
• časopisecké články MeSH