Electroencephalography
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Neurodevelopmental Correlates of the Emerging Adult Self T ⁎ Christopher G
Developmental Cognitive Neuroscience 36 (2019) 100626 Contents lists available at ScienceDirect Developmental Cognitive Neuroscience journal homepage: www.elsevier.com/locate/dcn Neurodevelopmental correlates of the emerging adult self T ⁎ Christopher G. Daveya,b,c, , Alex Fornitod,e, Jesus Pujolf, Michael Breakspearg,h, Lianne Schmaala,b, Ben J. Harrisonc a Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Australia b Centre for Youth Mental Health, The University of Melbourne, Parkville, Australia c Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Australia d Monash Clinical and Imaging Neuroscience, School of Psychological Sciences, Monash University, Clayton, Australia e Monash Biomedical Imaging, Monash University, Clayton, Australia f MRI Research Unit, Department of Radiology, Hospital del Mar, CIBERSAM G21, Barcelona, Spain g QIMR Berghofer Medical Research Institute, Brisbane, Australia h Hunter Medical Research Institute, University of Newcastle, Newcastle, Australia ARTICLE INFO ABSTRACT Keywords: The self-concept – the set of beliefs that a person has about themselves – shows significant development from Adolescent development adolescence to early adulthood, in parallel with brain development over the same period. We sought to in- Connectivity vestigate how age-related changes in self-appraisal processes corresponded with brain network segregation and Default mode network integration in healthy adolescents and young adults. We scanned 88 participants (46 female), aged from 15 to 25 Functional MRI years, as they performed a self-appraisal task. We first examined their patterns of activation to self-appraisal, and Self replicated prior reports of reduced dorsomedial prefrontal cortex activation with older age, with similar re- ductions in precuneus, right anterior insula/operculum, and a region extending from thalamus to striatum. -
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. -
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. -
CNS 2014 Program
Cognitive Neuroscience Society 21st Annual Meeting, April 5-8, 2014 Marriott Copley Place Hotel, Boston, Massachusetts 2014 Annual Meeting Program Contents 2014 Committees & Staff . 2 Schedule Overview . 3 . Keynotes . 5 2014 George A . Miller Awardee . 6. Distinguished Career Contributions Awardee . 7 . Young Investigator Awardees . 8 . General Information . 10 Exhibitors . 13 . Invited-Symposium Sessions . 14 Mini-Symposium Sessions . 18 Poster Schedule . 32. Poster Session A . 33 Poster Session B . 66 Poster Session C . 98 Poster Session D . 130 Poster Session E . 163 Poster Session F . 195 . Poster Session G . 227 Poster Topic Index . 259. Author Index . 261 . Boston Marriott Copley Place Floorplan . 272. A Supplement of the Journal of Cognitive Neuroscience Cognitive Neuroscience Society c/o Center for the Mind and Brain 267 Cousteau Place, Davis, CA 95616 ISSN 1096-8857 © CNS www.cogneurosociety.org 2014 Committees & Staff Governing Board Mini-Symposium Committee Roberto Cabeza, Ph.D., Duke University David Badre, Ph.D., Brown University (Chair) Marta Kutas, Ph.D., University of California, San Diego Adam Aron, Ph.D., University of California, San Diego Helen Neville, Ph.D., University of Oregon Lila Davachi, Ph.D., New York University Daniel Schacter, Ph.D., Harvard University Elizabeth Kensinger, Ph.D., Boston College Michael S. Gazzaniga, Ph.D., University of California, Gina Kuperberg, Ph.D., Harvard University Santa Barbara (ex officio) Thad Polk, Ph.D., University of Michigan George R. Mangun, Ph.D., University of California, -
A Resource for Assessing Information Processing in the Developing Brain Using EEG and Eye Tracking
bioRxiv preprint doi: https://doi.org/10.1101/092213; this version posted December 7, 2016. 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. TITLE A Resource for Assessing Information Processing in the Developing Brain Using EEG and Eye Tracking AUTHORS Nicolas Langer*3,1, Erica J. Ho*1,2, Lindsay M. Alexander1, Helen Y. Xu1, Renee K. Jozanovic1, Simon Henin5, Samantha Cohen5, Enitan T. Marcelle4,1, Lucas C. Parra5, Michael P. Milham1,6, Simon P. Kelly5,7 AFFILIATIONS 1. Center for the Developing Brain, Child Mind Institute, New York, NY, USA 2. Department of Psychology, Yale University, New Haven, CT, USA 3. Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland 4. Department of Psychology, University of California, Berkeley, USA 5. Department of Biomedical Engineering, City College of New York, New York, USA 6. Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, USA 7. School of Electrical and Electronic Engineering, University College Dublin, Ireland * These authors contributed equally to this work Corresponding author(s): Nicolas Langer ([email protected]), Michael P. Milham ([email protected]) and Simon P. Kelly ([email protected]) 1 bioRxiv preprint doi: https://doi.org/10.1101/092213; this version posted December 7, 2016. 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. -
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. -
EEG-Based Mental Workload Neurometric to Evaluate the Impact of Different Traffic and Road Conditions in Real Driving Settings
fnhum-12-00509 December 15, 2018 Time: 15:10 # 1 ORIGINAL RESEARCH published: 18 December 2018 doi: 10.3389/fnhum.2018.00509 EEG-Based Mental Workload Neurometric to Evaluate the Impact of Different Traffic and Road Conditions in Real Driving Settings Gianluca Di Flumeri1,2,3*, Gianluca Borghini1,2,3, Pietro Aricò1,2,3, Nicolina Sciaraffa1,2,4, Paola Lanzi5, Simone Pozzi5, Valeria Vignali6, Claudio Lantieri6, Arianna Bichicchi6, Andrea Simone6 and Fabio Babiloni1,3,7 1 BrainSigns srl, Rome, Italy, 2 IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Rome, Italy, 3 Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy, 4 Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, Rome, Italy, 5 Deep Blue srl, Rome, Italy, 6 Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), School of Engineering and Architecture, University of Bologna, Bologna, Italy, 7 Department of Computer Science, Hangzhou Dianzi University, Hangzhou, China Car driving is considered a very complex activity, consisting of different concomitant Edited by: tasks and subtasks, thus it is crucial to understand the impact of different factors, Muthuraman Muthuraman, University Medical Center of the such as road complexity, traffic, dashboard devices, and external events on the driver’s Johannes Gutenberg University behavior and performance. For this reason, in particular situations the cognitive demand Mainz, Germany experienced by the driver could be very high, inducing an excessive experienced Reviewed by: Edmund Wascher, mental workload and consequently an increasing of error commission probability. In Leibniz-Institut für Arbeitsforschung this regard, it has been demonstrated that human error is the main cause of the an der TU Dortmund (IfADo), 57% of road accidents and a contributing factor in most of them. -
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. -
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. -
Magnetoencephalography: Clinical and Research Practices
brain sciences Review Magnetoencephalography: Clinical and Research Practices Jennifer R. Stapleton-Kotloski 1,2,*, Robert J. Kotloski 3,4 ID , Gautam Popli 1 and Dwayne W. Godwin 1,5 1 Department of Neurology, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; [email protected] (G.P.); [email protected] (D.W.G.) 2 Research and Education, W. G. “Bill” Hefner Salisbury VAMC, Salisbury, NC 28144, USA 3 Department of Neurology, William S Middleton Veterans Memorial Hospital, Madison, WI 53705, USA; [email protected] 4 Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA 5 Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA * Correspondence: [email protected]; Tel.: +1-336-716-5243 Received: 28 June 2018; Accepted: 11 August 2018; Published: 17 August 2018 Abstract: Magnetoencephalography (MEG) is a neurophysiological technique that detects the magnetic fields associated with brain activity. Synthetic aperture magnetometry (SAM), a MEG magnetic source imaging technique, can be used to construct both detailed maps of global brain activity as well as virtual electrode signals, which provide information that is similar to invasive electrode recordings. This innovative approach has demonstrated utility in both clinical and research settings. For individuals with epilepsy, MEG provides valuable, nonredundant information. MEG accurately localizes the irritative zone associated with interictal spikes, often detecting epileptiform activity other methods cannot, and may give localizing information when other methods fail. These capabilities potentially greatly increase the population eligible for epilepsy surgery and improve planning for those undergoing surgery. MEG methods can be readily adapted to research settings, allowing noninvasive assessment of whole brain neurophysiological activity, with a theoretical spatial range down to submillimeter voxels, and in both humans and nonhuman primates. -
Experimental-Design Specific Changes in Spontaneous EEG And
NEUROSCIENCE RESEARCH ARTICLE V. V. Lazarev et al. / Neuroscience 426 (2020) 50–58 Experimental-design Specific Changes in Spontaneous EEG and During Intermittent Photic Stimulation by High Definition Transcranial Direct Current Stimulation V. V. Lazarev, a* N. Gebodh, b T. Tamborino, a M. Bikson b and E. M. Caparelli-Daquer c,d a Laboratory of Neurobiology and Clinical Neurophysiology, National Institute of Women, Children and Adolescents Health Fernandes Figueira, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil b Department of Biomedical Engineering, The City College of New York of The City University of New York, New York, NY, USA c Laboratory of Electrical Stimulation of the Nervous System, Department of Physiological Sciences and Neurosurgery Unit, Rio de Janeiro State University (UERJ), Rio de Janeiro, Brazil d Hospital Universita´rio Gaffre´e e Guinle, Federal University of the State of Rio de Janeiro (UniRio), Rio de Janeiro, Brazil Abstract—Electroencephalography (EEG) as a biomarker of neuromodulation by High Definition transcranial Direct Current Stimulation (HD-tDCS) offers promise as both techniques are deployable and can be integrated into a single head-gear. The present research addresses experimental design for separating focal EEG effect of HD- tDCS in the ‘4-cathode  1-anode’ (4  1) montage over the left motor area (C3). We assessed change in offline EEG at the homologous central (C3, C4), and occipital (O1, O2) locations. Interhemispheric asymmetry was accessed for background EEG at standard frequency bands; and for the intermittent photic stimulation (IPS). EEG was compared post- vs pre-intervention in three HD-tDCS arms: Active (2 mA), Sham (ramp up/down at the start and end), and No-Stimulation (device was not powered), each intervention lasting 20 min. -
Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: a Systematic Literature Review
sensors Systematic Review Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review Nuraini Jamil 1 , Abdelkader Nasreddine Belkacem 2,* , Sofia Ouhbi 1 and Abderrahmane Lakas 2 1 Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; [email protected] (N.J.); sofi[email protected] (S.O.) 2 Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; [email protected] * Correspondence: [email protected] Abstract: Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive Citation: Jamil, N.; Belkacem, A.N.; BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used Ouhbi, S.; Lakas, A. Noninvasive for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from Electroencephalography Equipment the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited for Assistive, Adaptive, and to studies focusing on applications and devices of the BCI technology.