(EEG)-Based Brain-Computer Interfaces Fabien Lotte, Laurent Bougrain, Maureen Clerc
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Towards an Objective Evaluation of EEG/MEG Source Estimation Methods: the Linear Tool Kit
bioRxiv preprint doi: https://doi.org/10.1101/672956; this version posted June 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Towards an Objective Evaluation of EEG/MEG Source Estimation Methods: The Linear Tool Kit Olaf Hauk1, Matti Stenroos2, Matthias Treder3 1 MRC Cognition and Brain Sciences Unit, University of Cambridge, UK 2 Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland 3 School of Computer Sciences and Informatics, Cardiff University, Cardiff, UK Corresponding author: Olaf Hauk MRC Cognition and Brain Sciences Unit University of Cambridge 15 Chaucer Road Cambridge CB2 7EF [email protected] Key words: inverse problem, point-spread function, cross-talk function, resolution matrix, minimum-norm estimation, spatial filter, beamforming, localization error, spatial dispersion, relative amplitude 1 bioRxiv preprint doi: https://doi.org/10.1101/672956; this version posted June 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract The question “What is the spatial resolution of EEG/MEG?” can only be answered with many ifs and buts, as the answer depends on a large number of parameters. Here, we describe a framework for resolution analysis of EEG/MEG source estimation, focusing on linear methods. -
Electroencephalography (EEG)-Based Brain Computer Interfaces for Rehabilitation
Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2012 Electroencephalography (EEG)-based brain computer interfaces for rehabilitation Dandan Huang Virginia Commonwealth University Follow this and additional works at: https://scholarscompass.vcu.edu/etd Part of the Biomedical Engineering and Bioengineering Commons © The Author Downloaded from https://scholarscompass.vcu.edu/etd/2761 This Dissertation is brought to you for free and open access by the Graduate School at VCU Scholars Compass. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of VCU Scholars Compass. For more information, please contact [email protected]. School of Engineering Virginia Commonwealth University This is to certify that the dissertation prepared by Dandan Huang entitled “Electroencephalography (EEG)-based brain computer interfaces for rehabilitation” has been approved by her committee as satisfactory completion of the dissertation requirement for the degree of Doctoral of Philosophy. Ou Bai, Ph.D., Director of Dissertation, School of Engineering Ding-Yu Fei, Ph.D., School of Engineering Azhar Rafiq, M.D., School of Medicine Martin L. Lenhardt, Ph.D., School of Engineering Kayvan Najarian, Ph.D., School of Engineering Gerald Miller, Ph.D., Chair, Department of Biomedical Engineering, School of Engineering Rosalyn Hobson, Ph.D., Associate Dean of Graduate Studies, School of Engineering Charles J. Jennett, Ph.D., Dean, School of Engineering F. Douglas Boudinot, Ph.D., Dean of the Graduate School ______________________________________________________________________ Date © Dandan Huang 2012 All Rights Reserved ELECTROENCEPHALOGRAPHY-BASED BRAIN-COMPUTER INTERFACES FOR REHABILITATION A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. -
Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals
sensors Article Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals Dong-Woo Koh 1, Jin-Kook Kwon 2 and Sang-Goog Lee 1,* 1 Department of Media Engineering, Catholic University of Korea, 43 Jibong-ro, Bucheon-si 14662, Korea; [email protected] 2 CookingMind Cop. 23 Seocho-daero 74-gil, Seocho-gu, Seoul 06621, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-2-2164-4909 Abstract: Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20–30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group’s correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group’s data were not. On the other hand, the elderly group’s data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing. -
Electroencephalography (EEG) Technology Applications and Available Devices
applied sciences Review Electroencephalography (EEG) Technology Applications and Available Devices Mahsa Soufineyestani , Dale Dowling and Arshia Khan * Department of Computer Science, University of Minnesota Duluth, Duluth, MN 55812, USA; soufi[email protected] (M.S.); [email protected] (D.D.) * Correspondence: [email protected] Received: 18 September 2020; Accepted: 21 October 2020; Published: 23 October 2020 Abstract: The electroencephalography (EEG) sensor has become a prominent sensor in the study of brain activity. Its applications extend from research studies to medical applications. This review paper explores various types of EEG sensors and their applications. This paper is for an audience that comprises engineers, scientists and clinicians who are interested in learning more about the EEG sensors, the various types, their applications and which EEG sensor would suit a specific task. The paper also lists the details of each of the sensors currently available in the market, their technical specs, battery life, and where they have been used and what their limitations are. Keywords: EEG; EEG headset; EEG Cap 1. Introduction An electroencephalography (EEG) sensor is an electronic device that can measure electrical signals of the brain. EEG sensors typically measure the varying electrical signals created by the activity of large groups of neurons near the surface of the brain over a period of time. They work by measuring the small fluctuations in electrical current between the skin and the sensor electrode, amplifying the electrical current, and performing any filtering, such as bandpass filtering [1]. Innovations in the field of medicine began in the early 1900s, prior to which there was little innovation due to the uncollaborative nature of the field of medicine. -
A National Review of Amplitude Integrated Electroencephalography (Aeeg) A
Issue: Ir Med J; Vol 113; No. 9; P192 A National Review of Amplitude Integrated Electroencephalography (aEEG) A. Jenkinson1, M.A. Boyle2, D. Sweetman1 1. The National Maternity Hospital, Dublin. 2. The Rotunda Hospital, Dublin. Dear Sir, The use of amplitude integrated encephalography (aEEG) has become well integrated into routine care in the neonatal intensive care unit (NICU), to monitor brain function where encephalopathy or seizures are suspected.1 Early aEEG abnormalities and their rate of resolution have clinical and prognostic significance in severely ill newborns with altered levels of consciousness. 2 The most common use of aEEG is in infants with neonatal encephalopathy undergoing therapeutic hypothermia (TH). While TH is performed in tertiary centres, local and regional centres play an important role in the diagnosis and initial management of these infants. The Therapeutic Hypothermia Working Group National Report from 2018 reported that 28/69 (40%) of infants requiring Therapeutic Hypothermia were outborn in local or regional units requiring transfer.3 The report also highlighted that early and accurate aEEG interpretation is important in the care of these infants with many seizures being sub-clinical or electrographic without clinical correlation. We performed a national audit on aEEG in neonatal services in Ireland, surveying 18 neonatal units that are divided into Level 1 (Local), Level 2 (Regional) and Level 3 (Tertiary). One unit was excluded from our study as it provides a continuous EEG monitoring service on a 24-hour basis as part of the Neonatal Brain Research Group. Our findings showed that while no Level 1 unit had aEEG, it is widely available in Level 2 and Level 3 units [3/4 (75%) and 3/3 (100%)] respectively. -
Analysis of EEG Signal Processing Techniques Based on Spectrograms
ISSN 1870-4069 Analysis of EEG Signal Processing Techniques based on Spectrograms Ricardo Ramos-Aguilar, J. Arturo Olvera-López, Ivan Olmos-Pineda Benemérita Universidad Autónoma de Puebla, Faculty of Computer Science, Puebla, Mexico [email protected],{aolvera, iolmos}@cs.buap.mx Abstract. Current approaches for the processing and analysis of EEG signals consist mainly of three phases: preprocessing, feature extraction, and classifica- tion. The analysis of EEG signals is through different domains: time, frequency, or time-frequency; the former is the most common, while the latter shows com- petitive results, implementing different techniques with several advantages in analysis. This paper aims to present a general description of works and method- ologies of EEG signal analysis in time-frequency, using Short Time Fourier Transform (STFT) as a representation or a spectrogram to analyze EEG signals. Keywords. EEG signals, spectrogram, short time Fourier transform. 1 Introduction The human brain is one of the most complex organs in the human body. It is considered the center of the human nervous system and controls different organs and functions, such as the pumping of the heart, the secretion of glands, breathing, and internal tem- perature. Cells called neurons are the basic units of the brain, which send electrical signals to control the human body and can be measured using Electroencephalography (EEG). Electroencephalography measures the electrical activity of the brain by recording via electrodes placed either on the scalp or on the cortex. These time-varying records produce potential differences because of the electrical cerebral activity. The signal generated by this electrical activity is a complex random signal and is non-stationary [1]. -
Using Amplitude-Integrated EEG in Neonatal Intensive Care
Journal of Perinatology (2010) 30, S73–S81 r 2010 Nature America, Inc. All rights reserved. 0743-8346/10 www.nature.com/jp REVIEW Using amplitude-integrated EEG in neonatal intensive care JD Tao and AM Mathur Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA monitoring can be applied, for example, to infants admitted with The implementation of amplitude-integrated electroencephalography encephalopathy, seizures or other brain malformations. Both term (aEEG) has enhanced the neurological monitoring of critically ill infants. and preterm infants may benefit from the clinical application of Limited channel leads are applied to the patient and data are displayed in a aEEG findings. Much similar to continuous monitoring of vital semilogarithmic, time-compressed scale. Several classifications are currently signs, bedside aEEG can help guide decision-making in real time in use to describe patient tracings, incorporating voltage criteria, pattern for infants admitted to the NICU. This article will review the recognition, cyclicity, and the presence or absence of seizures. In term history, classification and application of aEEG in both term and neonates, aEEG has been used to determine the prognosis and treatment for preterm infants. those affected by hypoxic–ischemic encephalopathy, seizures, meningitis and even congenital heart disease. Its application as inclusion criteria for therapeutic hypothermia remains controversial. In preterm infants, Background normative values and patterns corresponding to gestational age are being 2 established. As these standards emerge, the predictive value of aEEG Starting in the 1960s, Maynard et al., developed the first use of increases, especially in the setting of preterm brain injury and an instrument to study cerebral activity in resuscitated patients with intraventricular hemorrhage. -
Electroencephalography Source Connectivity: Toward High Time/Space Resolution Brain Networks
Electroencephalography source connectivity: toward high time/space resolution brain networks Hassan M.1, 2 and Wendling F.1, 2 1 University of Rennes 1, LTSI, F-35000 Rennes, France 2INSERM, U1099, F-35000 Rennes, France Abstract— The human brain is a large-scale network which function depends on dynamic interactions between spatially-distributed regions. In the rapidly-evolving field of network neuroscience, two yet unresolved challenges are potential breakthroughs. First, functional brain networks should be estimated from noninvasive and easy to use neuroimaging techniques. Second, the time/space resolution of these techniques should be high enough to assess the dynamics of identified networks. Emerging evidence suggests that Electroencephalography (EEG) source connectivity method may offer solutions to both issues provided that scalp EEG signals are appropriately processed. Therefore, the performance of EEG source connectivity method strongly depends on signal processing (SP) that involves various methods such as preprocessing techniques, inverse solutions, statistical couplings between signals and network science. The main objective of this tutorial-like review is to provide an overview on EEG source connectivity. We describe the major contributions that the SP community brought to this research field. We emphasize the methodological issues that need to be carefully addressed to obtain relevant results and we stress the current limitations that need further investigation. We also report results obtained in concrete applications, in both normal and pathological brain states. Future directions in term of signal processing methods and applications are eventually provided. Index Terms— EEG signal processing, functional brain networks, EEG source connectivity, statistical couplings, inverse solutions. 1 I. INTRODUCTION Over the past decades, neuroscience research has significantly improved our understanding of the normal brain. -
Optogenetic Investigation of Neuronal Excitability and Sensory
OPTOGENETIC INVESTIGATION OF NEURONAL EXCITABILITY AND SENSORY- MOTOR FUNCTION FOLLOWING A TRANSIENT GLOBAL ISCHEMIA IN MICE by Yicheng Xie Msc, The University of Missouri-Columbia, 2011 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Neuroscience) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2015 © Yicheng Xie, 2015 Abstract Global ischemia occurs during cardiac arrest and has been implicated as a complication that can occur during cardiac surgery. It induces delayed neuronal death in human and animal models, particularly in the hippocampus, while it also can affect the cortex. Other than morphology and measures of cell death, relatively few studies have examined neuronal networks and motor- sensory function following reversible global ischemia in vivo. Optogenetics allows the combination of genetics and optics to control or monitor cells in living tissues. Here, I adapted optogenetics to examine neuronal excitability and motor function in the mouse cortex following a transient global ischemia. Following optogenetic stimulation, I recorded electrical signals from direct stimulation to targeted neuronal populations before and after a 5 min transient global ischemia. I found that both excitatory and inhibitory neuronal network in the somatosensory cortex exhibited prolonged suppression of synaptic transmission despite reperfusion, while the excitability and morphology of neurons recovered rapidly and more completely. Next, I adapted optogenetic motor mapping to investigate the changes of motor processing, and compared to the changes of sensory processing following the transient global ischemia. I found that both sensory and motor processing showed prolonged impairments despite of the recovery of neuronal excitability following reperfusion, presumably due to the unrestored synaptic transmission. -
BIDS-EEG: an Extension to the Brain Imaging Data Structure (BIDS) Specification for Electroencephalography
BIDS-EEG: an extension to the Brain Imaging Data Structure (BIDS) Specification for electroencephalography Cyril R Perneta,∗, Stefan Appelhoffb,∗, Guillaume Flandinc, Christophe Phillipsd, Arnaud Delormee,f, Robert Oostenveldg,h aThe University of Edinburgh, Centre for Clinical Brain Sciences (CCBS), Chancellor’s Building, 49 Little France Crescent, Edinburgh EH16 4SB bCenter for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany cWellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK dGIGA Institute, University of Liège, Liège, Belgium eSCCN, INC, UCSD, La Jolla, CA 95404, USA fCerco, CNRS/UPS, Toulouse, France gRadboud University, Donders Institute for Brain, Cognition and Behaviour. P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands hNatMEG, Karolinska Institutet, Stockholm, Sweden Abstract The Brain Imaging Data Structure (BIDS) project is a quickly evolving effort among the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. The first BIDS standard was proposed for the MRI/fMRI research community and has now been widely adopted. More recently a magnetoencephalography (MEG) data extension, BIDS-MEG, has been published. Here we present an extension to BIDS for electroencephalography (EEG) data, BIDS-EEG, along with tools and references to a series of public EEG datasets organized using this new standard. Keywords: Electroencephalography, EEG, Brain Imaging Data Structure (BIDS), BIDS-EEG, standardization, datasets, formatting, archive Introduction Electroencephalography (EEG) is now almost a century old, with its first application in humans in the 1920s (Berger, 1929). EEG records the electric potential fluctuations at the scalp, primarily from locally synchronous post-synaptic activity in the apical dendrites of pyramidal cells in the cortex. -
Neurological Emergencies.” Postgraduate Medical Journal
“Should be read by those who decide initial management of neurological emergencies.” Postgraduate Medical Journal Since its birth as a series of articles in the Journal of Neurology, Neurosurgery and Psychiatry this book has moved into four editions, attesting to its lasting quality as an authoritative introduction to the major neurological emergencies. It is not just a basic reference for physicians, neurologists, neurosurgeons and psychiatrists in training. It has also become a standard text for emergency departments, with its N comprehensive yet concise discussions of immediate management. The conditions eurological covered are: ■ Medical coma ■ Cerebral infection ■ Traumatic brain injury ■ Acute spinal cord compression ■ Acute stroke ■ Acute neuromuscular respiratory ■ Delirium paralysis Neurological ■ Acute behaviour disturbances ■ Acute visual loss E ■ Tonic-clonic status epilepticus ■ Criteria for diagnosing brain mergencies ■ Raised intracranial pressure stem death Emergencies ■ Subarachnoid haemorrhage This edition has brought the book right up to date with emphasis on the evidence base for management, including reference to the relevant systematic reviews. With Fourth Edition the use of helpful summary boxes, this information – contributed by internationally respected leaders in their specialties – is quick to access in the clinical situation. Related titles from BMJ Books F The Epidemiology of Neurological Disorders ourth Edition Hughes Management of Neurological Disorders Neurological Investigations Edited by RAC Hughes Neurology and Medicine Stroke Units: an evidence based approach Neurology & Clinical Neurophysiology www.bmjbooks.com Neurological Emergencies Fourth edition Neurological Emergencies Fourth edition Edited by RAC Hughes Head, Department of Clinical Neurosciences, Guy’s, King’s and St Thomas’ School of Medicine, London, UK © BMJ Publishing Group 2003 BMJ Books is an imprint of the BMJ Publishing Group All rights reserved. -
Master's Specialisation in Neurophysics
Master’s specialisation in Neurophysics Prof dr AJ van Opstal April 2020 https://www.ru.nl/opleidingen/master/neurophysics Master’s specialisation in Neurophysics This specialisation is part of the Master’s programme(s) in Physics and Astronomy It offers a flexible and interdisciplinary programme, which challenges you to unravel the workings of the most complex system known to us, the human and animal brain, using experimental, theoretical, and computational methods from the natural sciences. Focus: This a unique program in the Netherlands. We cover the full spectrum from behavioural studies with human subjects (psychophysics), computational studies of advanced brain models (neuroinformatics, computational neuroscience), to theoretical studies on intelligent systems and behaviour (machine learning, robotics, artificial intelligence, data science), and electrophysiological studies (the ‘hardware’) For whom: Students with a bachelor diploma in Physics, Science, but also Biomedical Engineering, Mathematics, or similar Master’s specialisation in Neurophysics “To understand ANY information-processing system, it has to be studied at three complementary levels, all equally necessary” (David Marr, ‘82): • L1: What is the function of the system? Why does it do what it does? What benefit does it acquire? Which problem (usually, for survival) does it solve? How does it do it? In Neuroscience, this is the research topic of Psychophysics (the goal) • L2: What is the optimal algorithm, computational principle, that underlies the observed behavior? Importantly, how can the system acquire such behavior through unsupervised learning? This is the field of Computational neuroscience and Machine Learning (the software…) • • L3: How are the algorithm(s) and behaviour(s) implemented in the system? In Neuroscience, this is the topic of Neurophysiology (the hardware…) The Neurophysics specialisation at DCN studies all three levels.