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
Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.
- 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.
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
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
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.
- Publication type
- Journal Article MeSH
Studie se zaměřuje na vliv počítačového tréninku pomocí programu HAPPYneuron Brain Jogging na zlepšení kognitivních funkcí (paměti a pozornosti) u osob po cévní mozkové příhodě a po traumatickém poškození mozku. Zúčastnilo se jí 44 pacientů (28 mužů, 16 žen 22 CMPprůměrný věk 43 let)22 TBI, kteří byli rozděleni do experimentální (N = 28) a kontrolní (N = 16) skupiny. Cílem projektu bylo porovnání výsledků v kognitivních testech (AVLT, WAIS-III subtest Opakování čísel, LGT3 subtest Předměty, TMT (A, B), Číselný čtverec, Digit symbol, Verbální fluence) a v sebeposuzovacích škálách (Dotazník kognitivních selhání, Schwartzova škála, Dysexekutivní dotazník) před a po dvouměsíčním intenzivním tréninku. Trénink probíhal v domácím prostředí a v jeho rámci měli účastníci odehrát v programu HAPPYneuron Brain Jogging 400 her se zaměřením na paměť a pozornost, což odpovídalo přibližně rozsahu 24 hodin. Na základě zpracování dat Wilcoxonovým párovým testem (p = 0,05) se ukázalo, že experimentální skupina dosáhla signifikantně vyššího zlepšení u pěti ze třinácti sledovaných testových subskórů a u dvou subjektivních dotazníků.
Der Studienschwerpunkt ist die Auswirkung der Benutzung des Computertrainingsprogramm Happyneuron Brain Jogging auf Verbesserungen der kognitiven Funktionen (Gedächtnis und Aufmerksamkeit) bei Schlaganfall- Patienten und bei Patienten, die traumatische Hirnverletzungen erlitten haben. Wir haben 44 Patienten (28 Männer, 16 Frauen; 22 Schlaganfall-Patienten, 22 TBI Patienten im Durchschnittsalter 43 Jahre) untersucht. Alle Teilnehmer wurden in eine experimentelle (N = 28) und eine Kontrollgruppe (N = 16) eingeteilt. Das Ziel unserer Studie war es, die Ergebnisse in kognitiven Tests (AVLT , WAIS-III Subtest Zahlen, LGT3 Subtest Objekte, TMT (A, B), Numerisches Quadrat, Digit Symbol, Verbal Fluency) mit Selbstbeurteilungsskalen (Cognitive Failure Questionnaire, Schwartz Outcomes Scale, Dysexecutive Questionnaire) vor und nach zwei Monaten intensiven Trainings zu vergleichen. Das Training wurde individuell zu Hause gemacht und besteht aus 400 Gedächtnis-und Aufmerksamkeitsspielen im Umfang ca. 24 Stunden. Es wurde der Wilcoxon Paartest angewendet (p = 0,05) und er zeigte signifikante Verbesserung in fünf von dreizehn Test-Subscoren und in zwei Selbstbeurteilungsskalen.
- MeSH
- Time Factors MeSH
- Stroke MeSH
- Adult MeSH
- Cognition Disorders * etiology rehabilitation MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Neuropsychological Tests statistics & numerical data MeSH
- Brain Injuries rehabilitation MeSH
- Memory Disorders rehabilitation MeSH
- Stroke Rehabilitation MeSH
- Aged MeSH
- User-Computer Interface * MeSH
- Video Games * psychology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Aged MeSH
- Female 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
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.
