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
- Algorithms MeSH
- Artifacts MeSH
- Electrocardiography MeSH
- Essential Tremor * MeSH
- Humans MeSH
- Brain-Computer Interfaces * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't 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.
- MeSH
- Algorithms * MeSH
- Artifacts MeSH
- Electroencephalography * methods MeSH
- Humans MeSH
- Brain * physiology MeSH
- Brain-Computer Interfaces MeSH
- Machine Learning MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review 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.
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.
Archives of physical medicine and rehabilitation, ISSN 0003-9993 Volume 96, no. 3, March 2015, supplement 1
87 stran : ilustrace, tabulky ; 28 cm
- MeSH
- Electroencephalography MeSH
- Rehabilitation MeSH
- Reproducibility of Results MeSH
- Brain-Computer Interfaces MeSH
- Translational Research, Biomedical MeSH
- Publication type
- Collected Work MeSH
- Conspectus
- Patologie. Klinická medicína
- NML Fields
- neurologie
- rehabilitační a fyzikální medicína
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.
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
- Spectroscopy, Near-Infrared methods MeSH
- Diagnostic Techniques, Neurological MeSH
- Electroencephalography methods instrumentation MeSH
- Electrooculography methods MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Magnetoencephalography methods MeSH
- Neuroimaging MeSH
- Positron-Emission Tomography MeSH
- Brain-Computer Interfaces * MeSH
- Evoked Potentials, Visual MeSH
- Check Tag
- Humans MeSH
- Keywords
- neuronální implantáty, nahrávání informací do mozku, neurotechnologie, propojení mozku s počítačem,
- MeSH
- Implants, Experimental MeSH
- Animal Experimentation MeSH
- Haplorhini MeSH
- Deep Brain Stimulation MeSH
- Humans MeSH
- Brain MeSH
- Neural Conduction MeSH
- Therapy, Computer-Assisted MeSH
- Brain-Computer Interfaces * utilization MeSH
- Research MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
BACKGROUND: Evidence suggests that brain-computer interface (BCI)-based rehabilitation strategies show promise in overcoming the limited recovery potential in the chronic phase of stroke. However, the specific mechanisms driving motor function improvements are not fully understood. OBJECTIVE: We aimed at elucidating the potential functional brain connectivity changes induced by BCI training in participants with chronic stroke. METHODS: A longitudinal crossover design was employed with two groups of participants over the span of 4 weeks to allow for within-subject (n = 21) and cross-group comparisons. Group 1 (n = 11) underwent a 6-day motor imagery-based BCI training during the second week, whereas Group 2 (n = 10) received the same training during the third week. Before and after each week, both groups underwent resting state functional MRI scans (4 for Group 1 and 5 for Group 2) to establish a baseline and monitor the effects of BCI training. RESULTS: Following BCI training, an increased functional connectivity was observed between the medial prefrontal cortex of the default mode network (DMN) and motor-related areas, including the premotor cortex, superior parietal cortex, SMA, and precuneus. Moreover, these changes were correlated with the increased motor function as confirmed with upper-extremity Fugl-Meyer assessment scores, measured before and after the training. CONCLUSIONS: Our findings suggest that BCI training can enhance brain connectivity, underlying the observed improvements in motor function. They provide a basis for developing novel rehabilitation approaches using non-invasive brain stimulation for targeting functionally relevant brain regions, thereby augmenting BCI-induced neuroplasticity and enhancing motor recovery.
- MeSH
- Stroke * physiopathology diagnostic imaging MeSH
- Chronic Disease MeSH
- Default Mode Network * physiopathology diagnostic imaging MeSH
- Adult MeSH
- Cross-Over Studies MeSH
- Middle Aged MeSH
- Humans MeSH
- Longitudinal Studies MeSH
- Magnetic Resonance Imaging MeSH
- Brain * physiopathology diagnostic imaging MeSH
- Nerve Net * physiopathology diagnostic imaging MeSH
- Stroke Rehabilitation * methods MeSH
- Brain-Computer Interfaces * MeSH
- Aged MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- MeSH
- Amyotrophic Lateral Sclerosis * epidemiology etiology genetics prevention & control therapy MeSH
- Exercise MeSH
- Genetic Therapy MeSH
- Rodentia MeSH
- Humans MeSH
- Disease Models, Animal MeSH
- Motor Neurons parasitology MeSH
- Mice MeSH
- Neuroprotective Agents therapeutic use MeSH
- Nerve Growth Factors therapeutic use MeSH
- Brain-Computer Interfaces MeSH
- Superoxide Dismutase genetics MeSH
- RNAi Therapeutics MeSH
- Stem Cell Transplantation MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Mice MeSH
- Animals MeSH
