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.

• 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

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.

• MeSH
• algoritmy MeSH
• elektroencefalografie metody   MeSH
• mozek MeSH
• neuronové sítě MeSH
• rozhraní mozek-počítač * 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.

• 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

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.

• 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

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.

• 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

The goal of every human being on our planet is to improve the living conditions not only of his life, but also of all humanity. Digitization, dynamic development of technological equipment, unique software solutions and the transfer of human capabilities into the form of data enable the gradual achievement of this goal. The human brain is the source of all activities (physical, mental, decision-making, etc.) that a person performs. Therefore, the main goal of research is its functioning and the possibility to at least partially replace this functioning by external devices connected to a computer. The Brain-Computer Interface (BCI) is a term which represents a tool for performing external activities through sensed signals from the brain. This document describes various techniques that can be used to collect the neural signals. The measurement can be invasive or non-invasive. Electroencephalography (EEG) is the most studied non-invasive method and is therefore described in more detail in the presented paper. Once the signals from the brain are scanned, they need to be analysed in order to interpret them as computer commands. The presented methods of EEG signal analysis have advantages and disadvantages, either temporal or spatial. The use of the inverse EEG problem can be considered as a new trend to solve non-invasive high-resolution BCI.

• MeSH
• blízká infračervená spektroskopie metody   MeSH
• diagnostické techniky neurologické MeSH
• elektroencefalografie metody   přístrojové vybavení   MeSH
• elektrookulografie metody   MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• magnetoencefalografie metody   MeSH
• neurozobrazování MeSH
• pozitronová emisní tomografie MeSH
• rozhraní mozek-počítač * MeSH
• zrakové evokované potenciály MeSH
• Check Tag
• lidé MeSH

• MeSH
• elektroencefalografie MeSH
• rehabilitace MeSH
• reprodukovatelnost výsledků MeSH
• rozhraní mozek-počítač MeSH
• translační biomedicínský výzkum MeSH
• Publikační typ
• sborníky MeSH

• Konspekt
• Patologie. Klinická medicína
• NLK Obory
• neurologie
• rehabilitační a fyzikální medicína

• Klíčová slova
• neuronální implantáty, nahrávání informací do mozku, neurotechnologie, propojení mozku s počítačem,
• MeSH
• experimentální implantáty MeSH
• experimenty na zvířatech MeSH
• Haplorrhini MeSH
• hluboká mozková stimulace MeSH
• lidé MeSH
• mozek MeSH
• nervové vedení MeSH
• počítačem asistovaná terapie MeSH
• rozhraní mozek-počítač * využití   MeSH
• výzkum MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH

• MeSH
• amyotrofická laterální skleróza * epidemiologie   etiologie   genetika   prevence a kontrola   terapie   MeSH
• cvičení MeSH
• genetická terapie MeSH
• hlodavci MeSH
• lidé MeSH
• modely nemocí na zvířatech MeSH
• motorické neurony parazitologie   MeSH
• myši MeSH
• neuroprotektivní látky terapeutické užití   MeSH
• neurotrofní faktory terapeutické užití   MeSH
• rozhraní mozek-počítač MeSH
• superoxiddismutasa genetika   MeSH
• terapie založená na RNAi MeSH
• transplantace kmenových buněk MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• myši MeSH
• zvířata MeSH