DOCSLIB.ORG

Ecog Signal Processing for Brain Computer Interface with Multiple Degrees of Freedom for Clinical Application Marie-Caroline Schaeffer

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Ecog Signal Processing for Brain Computer Interface with Multiple Degrees of Freedom for Clinical Application Marie-Caroline Schaeffer ECoG signal processing for Brain Computer Interface with multiple degrees of freedom for clinical application Marie-Caroline Schaeffer To cite this version: Marie-Caroline Schaeffer. ECoG signal processing for Brain Computer Interface with multiple degrees of freedom for clinical application. Medical Physics [physics.med-ph]. Université Grenoble Alpes, 2017. English. NNT : 2017GREAS026. tel-01763451 HAL Id: tel-01763451 https://tel.archives-ouvertes.fr/tel-01763451 Submitted on 11 Apr 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE Pour obtenir le grade de DOCTEUR DE LA COMMUNAUTE UNIVERSITE GRENOBLE ALPES Spécialité : MBS – Modèles, méthodes et algorithmes en biologie, santé et environnement Arrêté ministériel : 25 mai 2016 Présentée par Marie-Caroline SCHAEFFER Thèse dirigée par Tetiana AKSENOVA (EDISCE), CEA préparée au sein du Laboratoire CLINATEC dans l'École Doctorale Ingénierie pour la Santé, la Cognition et l’Environnement Traitement du signal ECoG pour Interface Cerveau Machine à grand nombre de degrés de liberté pour application clinique Thèse soutenue publiquement le 6 Juin 2017 , devant le jury composé de : Prof. Alim-Louis BENABID Président Dr. Laurent BOUGRAIN Membre Prof. François CABESTAING Rapporteur Dr. Marco CONGEDO Membre Prof. Saïd MOUSSAOUI Rapporteur Prof. Laura SACERDOTE Membre i Acknowledgements I feel very grateful to the members of my committee, Prof. Alim-Louis BENABID, Dr. Laurent BOUGRAIN, Dr. Marco CONGEDO and Prof. Laura SACERDOTE. My special thanks go to Prof. François CABESTAING and Prof. Saïd MOUSSAOUI for having accepted to act as reporters of this doctoral work. My deepest gratitude goes to Dr. Tetiana AKSENOVA who has supervised this doctoral work. I wouldn’t have been able to complete this work without her constant guidance, insight, availability, patience and kindness. It has been a great privilege to work and progress under her supervision. I would also like to thank Guillaume CHARVET who co-supervised this doctoral work, and Prof. Alim-Louis BENABID and Corinne MESTAIS who head CLI- NATEC’s BCI project. Their suggestions have always been helpful and illuminating. It is a great pleasure to me to extend my thanks to all the associates of CLINATEC with whom I worked, had insightful discussions with or with whom I simply enjoyed meals or coffee breaks. Their presence has helped me along more than once. Finally, I would like to thank my family and friends for their unwavering support throughout these last three years. iii Preface The present doctoral work, which has been supervised by Dr. T. AKSENOVA and co-supervised by G. CHARVET, has been prepared at CLINATEC, an Edmond J. SAFRA Biomedical research centre located on the MINATEC Campus at CEA/LETI in Grenoble. CLINATEC associates medical research and technological innovation to translate new solutions to patients, with the purpose of accelerating the development and clinical validation of innovative medical devices. This doctoral work has been completed within the framework of CLINATEC’s motor Brain-Computer-Interface project, which is supervised by Prof. A.-L. BENABID, C. MESTAIS and G. CHARVET. The goal of this project is to bring the proof of concept that it is feasible for a tetraplegic subject to control complex effectors, for example a 4-limb exoskeleton, thanks to the monitoring and decoding of his brain activity. CLINATEC’s "BCI and Tetraplegia" 5-year clinical trial has recently been authorized by the French regulatory agencies. Signal processing challenges specific to the clinical deployment of CLINATEC’s Brain-Computer-Interface system, namely asynchronous mono- limb decoding, asynchronous sequential multi-limb decoding and decoding accuracy during active states, have been addressed in the present doctoral thesis. Parts of the presented work have been published in journals or conference proceedings, or exposed during oral presentations: Journal Schaeffer, M. C., & Aksenova, T. (2017). Switching Markov Decoders for Asyn- chronous Trajectory Reconstruction from ECoG signals in Monkeys for BCI Applica- tions. Journal of Physiology-Paris. doi: 10.1016/j.jphysparis.2017.03.002; Published online: 10 March 2017. International conference proceedings Schaeffer, M. C., & Aksenova, T. (2016, September). Hybrid Trajectory Decoding from ECoG Signals for Asynchronous BCIs. In International Conference on Artificial Neural Networks, Barcelona, Spain (pp. 288-296). Springer International Publishing. Patent pending Schaeffer, M. C., & Aksenova, T. (June 2016). Interface neuronale directe à décodage continu utilisant un mélange markovien d’experts. Oral communications M.C. Schaeffer, T. Aksenova. Markovian Mixture of Experts for 3D wrist movement decoding from ECoG recordings. GDR 2904 Multielectrode systems for Neuroscience: 6th annual meeting, 5-8 Jan 2016, Grenoble-Autrans. (speaker) iv M.C. Schaeffer, E. Labyt, V. Rohu, N. Tarrin, I. Vergara, S. Cokgungor, A. Eliseyev, G. Charvet, C. Mestais, A.L. Benabid, T. Aksenova. Hand movement decoding from magnetoencephalographic signals for BCI applications. Société de Neurophysiologie Clinique de Langue Française (SNCLF) 2015, 1-3 Juillet 2015, Grenoble. M.C. Schaeffer, S. Cokgungor, N. Tarrin, V. Rohu, E. Labyt, C. Mestais, A.L. Ben- abid, T. Aksenova. Decoding of 3D hand movements from Magnetoencephalography recordings of brain neuronal activity: preliminary results. BioHealth Computing Workshop, 1-3 Septembre 2014, Grenoble. (speaker) v Résumé Introduction Les Interfaces Cerveau-Machine (ICM) sont des systèmes qui per- mettent à des patients souffrant d’un handicap moteur sévère d’interagir avec leur environnement en utilisant leur activité cérébrale pour contrôler des effecteurs ex- térieurs. Plusieurs étapes sont généralement nécessaires pour convertir l’activité cérébrale du patient en commandes permettant de contrôler un effecteur extérieur. Des caractéristiques spécifiques aux intentions de l’utilisateur sont tout d’abord extraites de son activité cérébrale, préalablement acquise et digitalisée. Un décodeur est appliqué sur ces caractéristiques cérébrales, et permet de les convertir en esti- mations des intentions du sujet. Après une étape optionnelle de post-traitement susceptible d’améliorer la qualité des estimations, les intentions décodées du sujet sont converties en commandes utilisées pour contrôler le ou les effecteur(s) de l’ICM, par exemple des orthèses ou des prothèses de membres inférieurs ou supérieurs dans le cas d’ICM motrices. L’objectif de l’ICM motrice de CLINATEC est de permettre à des patients tétraplégiques de recouvrer de façon chronique une indépendance motrice en mod- ulant leur activité électrocorticographique (ECoG) pour contrôler des effecteurs complexes tels qu’un exosquelette 4 membres. Des challenges spécifiques au dé- ploiement clinique d’ICMs multi-effecteurs ont été étudiés dans la présente thèse de doctorat. Un problème majeur des ICMs cliniques est la capacité à proposer aux utilisateurs un contrôle asynchrone sur l’effecteur. Contrairement aux ICMs synchrones qui sont périodiquement contrôlables par les utilisateurs et requièrent donc la présence d’un opérateur pour activer et désactiver le système, les ICMs asynchrones sont disponibles en continu. Lorsqu’une ICM fonctionne en mode asynchrone, il est particulièrement souhaitable de limiter des activations erronées de l’effecteur pendant les périodes dites de Non-Contrôle (NC). Un deuxième challenge résulte de la possible présence d’un effecteur multi-membres, qui rend nécessaire la généralisation du contrôle asynchrone mono-membre au cas multi-membres. Une stratégie d’activation séquentielle a été considérée dans la présente thèse, avec le but d’améliorer la robustesse du système et de faciliter le contrôle cérébral. Au-delà de la limitation des activations erronées du système, il est nécessaire d’éviter des mouvements parallèles résiduels d’effecteurs momentanément non-contrôlés. Finalement, la capacité des utilisateurs à exécuter des mouvements contrôlés par leur activité cérébrale est compromise quand les estimations de leurs intentions de mouvements ne sont pas suffisamment proches de leurs véritables intentions. La précision du décodage pendant les périodes dites de Contrôle Intentionnel (Intentional Control, IC) est donc essentielle. Décodeurs pour ICM motrices précises et asynchrones Différents outils al- gorithmiques sont utilisés dans la communauté des ICMs pour assurer une conversion pertinente des signaux cérébraux des sujets en estimations de leurs intentions, tout particulièrement des modèles permettant d’estimer la valeur de variables discrètes (classifieurs) ou continues (modèles de régression ou filtres bayésiens) à partir des vi signaux cérébraux. Deux stratégies de décodage sont principalement exploitées par les ICMs motrices. La première consiste à demander au sujet d’effectuer des tâches mentales (tout particulièrement des imaginations motrices ou des tâches cognitives, par exemple des calculs mentaux), qui sont généralement chacune associées à une direction possible de l’effecteur. La seconde vise à directement
Load more

Recommended publications
  • ENG 384 Assignment.Odt
    PLANNING AND CONSTRUCTION OF MECHATRONICS ENGINEERING FACILITIES FOR SUSTAINABLE DEVELOPMENT IN NIGERIA; CHALLENGES AND WAY FORWARD BY SAJINYAN, OLANREWAJU OLAKUNLE 18/ENG05/055 SUBMITTED TO MECHANICAL AND MECHATRONICS DEPARTMENT COLLEGE OF ENGINEERING, ABUAD IN PARTIAL FULFILLMENT OF ENG 384: ENGINEERING LAW 2ND MAY, 2021 ABSTRACT With time, technology and engineering keep progressing across the world. In ancient times, it started with only civil engineering which involved the building of structures like houses for shelter or tools to make farming easier. Now there’s a wide range of individual engineering studies one can dive into inside this modern age. Some examples being Electrical engineering, mechanical engineering, computer engineering, even software engineering. Some of these modern age engineering programs can be further divided into other engineering categories Mechatronics engineering included. Other engineering disciplines from these categories include: Bio-Medical engineering, Structural engineering, Aeronautical Engineering. In this paper, planning and construction of Mechatronics engineering structures and facilities for sustainable development in Nigeria; its challenges as well as its way forward will be treated. INTRODUCTION TO MECHATRONICS ENGINEERING Mechatronics, also called mechatronics engineering, is an interdisciplinary branch of engineering that focuses on the engineering of electronic, electrical and mechanical engineering systems, and also includes a combination of robotics, electronics, computer, telecommunications,
    [Show full text]
  • Human Enhancement Technologies and Our Merger with Machines
    Human Enhancement and Technologies Our Merger with Machines Human • Woodrow Barfield and Blodgett-Ford Sayoko Enhancement Technologies and Our Merger with Machines Edited by Woodrow Barfield and Sayoko Blodgett-Ford Printed Edition of the Special Issue Published in Philosophies www.mdpi.com/journal/philosophies Human Enhancement Technologies and Our Merger with Machines Human Enhancement Technologies and Our Merger with Machines Editors Woodrow Barfield Sayoko Blodgett-Ford MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin Editors Woodrow Barfield Sayoko Blodgett-Ford Visiting Professor, University of Turin Boston College Law School Affiliate, Whitaker Institute, NUI, Galway USA Editorial Office MDPI St. Alban-Anlage 66 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Philosophies (ISSN 2409-9287) (available at: https://www.mdpi.com/journal/philosophies/special issues/human enhancement technologies). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. Journal Name Year, Volume Number, Page Range. ISBN 978-3-0365-0904-4 (Hbk) ISBN 978-3-0365-0905-1 (PDF) Cover image courtesy of N. M. Ford. © 2021 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.
    [Show full text]
  • Rehabilitation Technologies: Biomechatronics Point of View
    1 Rehabilitation Technologies: Biomechatronics Point of View Erhan Akdoğan and M. Hakan Demir Yıldız Technical University Turkey 1. Introduction Rehabilitation aims to bring back the patient’s physical, sensory, and mental capabilities that were lost due to injury, illness, and disease, and to support the patient to compensate for deficits that cannot be treated medically (http://www.ehendrick.org/healthy, June 2010). After the Spinal Cord Injury (SCI), stroke, muscle disorder, and surgical operation such as knee artroplasticy, patients need rehabilitation to recover their movement capability (mobilization) (Bradly et al., 2000; Inal, 2000; Metrailler et al., 2007; Okada et al., 2000; Reinkensmeyer, 2003 and http://www.manchesterneurophysio.co.uk, November 2010). The number of those who need rehabilitation is steadily increasing everyday. Parallel to this, equipment and techniques used in the field of rehabilitation are becoming more advanced and sophisticated. On the other hand, mechatronics, an interdisciplinary science, is a combination of machinery, electric-electronics and computer sciences plays an important role in rehabilitation technologies. In particular mechatronics systems provide important benefits for movements that are related to physical exercises in rehabilitation process. Biomechatronics is a sub-discipline of mechatronics. It is related to develop mechatronics systems which assist or restore to human body. A biomechatronic system has four units: Biosensors, Mechanical Sensors, Controller, and Actuator. Biosensors detect intentions of human using biological reactions coming from nervous or muscle system. The controller acts as a translator between biological and electronic systems, and also monitors the movements of the biomechatronic device. Mechanical sensors measure information about the biomechatronic device and relay to the biosensor or controller.
    [Show full text]
  • 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.
    [Show full text]
  • Chapter 23. Lower Limb Active Prosthetic Systems—Overview
    CHAPTER LOWER LIMB ACTIVE PROSTHETIC SYSTEMS— OVERVIEW 23 Alexandra S. Voloshina and Steven H. Collins Department of Mechanical Engineering, Stanford University, Stanford, CA, United States 23.1 INTRODUCTION Changes in lower limb mechanics, sensory feedback, and power output associated with lower limb loss have substantial impacts on the gait biomechanics and energetics of individuals with amputa- tions. During unimpaired level ground walking, the ankle produces significant net positive ankle power throughout the stride [1,2], with other locomotor tasks, such as walking up slopes, relying on additional net positive power output at the knee [3]. As a result, lower limb amputation and the associated loss of ankle and knee power production is linked to slower self-selected walking speeds and increased energy expenditure [4]. People with transtibial and transfemoral amputation expend up to 30% and 60% more metabolic energy, respectively [4,5], when compared to unimpaired indi- viduals walking at the same speed. Similarly, the preferred walking speed of individuals with amputation can be 10%À65% slower than the average walking speed of unaffected individuals, depending on the level of amputation and the walking surface [4,6]. Individuals with lower limb amputation often adapt compensatory gait strategies that can lead to significant changes in gait dynamics, joint loading and work, and muscle activity in the affected and unaffected limbs. For example, unilateral below-knee prosthesis users tend to favor their unaf- fected limb [7,8], which often endures greater joint forces, moments, and stress during daily activity [9À11]. The hip joint on the contralateral limb can produce up to three times more work than the hip joint of an unimpaired individual, likely as compensation for lack of power production of con- ventional passive prostheses [1,12].
    [Show full text]
  • Curriculum Vitae
    Curriculum Vitae Sudeep Sarkar Professor and Chair, Department of Computer Science and Engineering University of South Florida 4202 East Fowler Ave., ENB 118 Tampa, Florida 33620 E-mail: [email protected] http://www.cse.usf.edu/˜sarkar/ October 13, 2017 To teach, to seek, to serve, & to learn. 1. Educational Background • Doctor of Philosophy (Electrical Engineering) o The Ohio State University, Columbus, Ohio, March 1993. o Dissertation title: On Computing Perceptual Organization in Computer Vision. o Advisor: Prof. Kim L. Boyer. • Master of Science (Electrical Engineering) o The Ohio State University, Columbus, Ohio, March 1990. o Thesis: Optimal, Efficient Detection and Low Level Perceptual Organization of Edge Features. o Advisor: Prof. Kim L. Boyer. • Bachelor of Technology (Electrical Engineering) o Indian Institute of Technology, Kanpur, India, May 1988. o Project: Modeling of flicker noise using autoregressive moving average (ARMA) models: Simulation and Hardware Validation. o Project Advisor: Prof R. Sharan. 2. Honors 1. Fellow of the National Academy of Inventors (NAI), 2017. 2. Fellow of the American Institute for Medical and Biological Engineering (AIMBE), 2016. 3. Best Student Paper Award at IEEE International Conference on Identity, Security, and Behavior Analysis, 2015. 4. Best Scientific Paper Award at International Conference on Pattern Recognition, 2014. 5. Outstanding Faculty Award, University of South Florida, 2014. 6. Fellow of the American Association for the Advancement of Sciences (AAAS), 2013. 7. Fellow of the Institute for Electrical and Electronics Engineering (IEEE), 2013. 8. IEEE Computer Society Distinguished Visitor Program Speaker, 2010-2012. 9. Charter Member of National Academy of Inventors, 2010. 10. IBM Best Student Paper Award at International Conference on Pattern Recognition, 2010.
    [Show full text]
  • Non Invasive Brain-Machine Interfaces Final Report
    Non Invasive Brain-Machine Interfaces Final Report Authors: José del R. Millán, Pierre W. Ferrez, Anna Buttfield Affiliation: IDIAP Research Institute ESA Research Fellow/Technical Officer: Carlo Menon Contacts: Prof. Dr. José del R. Millán Tel: +41-27-7217770 Fax: +41-27-7217712 e-mail: [email protected] Dr. Carlo Menon Tel: +31(0)715658675 Fax: +31(0)715658018 e-mail: [email protected] Ariadna ID: 05/6402 Available on the ACT website Study Duration: 2 months http://www.esa.int/act Contract Number: 19708/06/NL/HE Table of Contents 1. Introduction............................................................................................. 3 2. EEG-based Brain-Computer Interfaces: Methodologies ........................ 6 2.1 Evoked BCIs ................................................................................... 6 2.2 Spontaneous BCI............................................................................. 7 2.2.1 Slow Potentials................................................................. 8 2.2.2 Rhythmic Activity............................................................ 9 2.3 Operant Conditioning & Machine Learning ................................. 11 2.4 Synchronous vs. Asynchronous BCI............................................. 12 2.5 Spatial Filtering............................................................................. 13 3. Hardware............................................................................................... 14 4. Current Applications............................................................................
    [Show full text]
  • Pdf Biomed Tech 2014; 59 (S1) © 2014 by Walter De Gruyter • Berlin • Boston
    Biomed Tech 2014; 59 (s1) © 2014 by Walter de Gruyter • Berlin • Boston. DOI 10.1515/bmt-2014-4445 S1024 Development of an implantable integrated MEMS pressure sensor us- ing polyimide epoxy composite and Atomic Layer Deposition for en- capsulation P. Gembaczka1, M. Görtz1 and M. Kraft1, 1Fraunhofer Institute for Microelectronic Circuits and Systems, Duisburg, Germany, [email protected] Introduction The futher miniaturization of MEMS sensors (micro-electro-mechanical system) opens up a wide range of medical im- plants applications. Typical medically approved encapsulation materials are titanium or ceramic, which have thick layer (~100µm) and hence are relatively large. For MEMS pressure sensors the membranes must be free to move and in con- tact with tissues or body fluids. A thin and hermetically sealing encapsulation adapting to the shape of the MEMS sen- sor is needed. Flexible encapsulation based on silicone is no hermetic sealing. Here, a novel encapsulation method is demonstrated for a capacitive MEMS pressure sensor system. A high temperature resistant polyimide-epoxy composite was used as die attach material and as sealing compound for bond wires and parts of the chip surface (figure 1). This was followed by an atomic-layer-deposition (ALD) of aluminium oxide (Al2O3) and tantalum pentoxide (Ta2O5) (figure 2) providing a hermetically sealed encapsulation. The polyimide-epoxy is compatible with the ALD deposition tempera- ture of 275°C for several hours. Methods To locate even the smallest defects in the passivation a destructive amperometric measurement method in phosphate buffered saline (PBS) was chosen. To test the performance and repeatability, the pressure measurement system was op- erated and calibrated in a 0.9% saline solution (figure 3).
    [Show full text]
  • Course Outline: Biomechatronic Systems Course Description
    K. N. Toosi University of Technology Faculty of Electrical Engineering Course Outline: Biomechatronic Systems Course Description This course will cover the interdisciplinary elements of biomechatronic systems and provides insight into the diverse applications of current biomechatronic technologies. Most lectures incorporate examples of emerging research and development activities across the medical and engineering fields. Course Instructor Dr. Mehdi Delrobaei Email: [email protected] Phone: 84062450 Ex. 304 Course Evaluation Assignments: 10 % Mid-Term Exam: 25 % Project: 25 % Final Exam: 40 % Lectures Week 1: Introduction to Biomechatronics Bio-mechanics, Bio-electrics, Bionics, and Bio-mechatronics Physiological and Bio-mechanical Systems The Human Factors: Stimulus, Sensing, and Actuation Nervous System Musculoskeletal System Safety and Ethical Aspects Week 2: Signal Processing Biomedical and Bioelectric Signals Signal Acquisition Amplifiers and Noise Time Domain Analysis Frequency Domain Analysis Practical Considerations Weeks 3-4: Sensors and Actuators Simple Sensors: Switches, Resistive, Capacitive, Inductive, Magnetic Sonar and Optical Sensors Inertial Measurement Units Temperature, Pressure, and Tactile Sensing Body-Surface Biopotential Electrodes Simple Actuators: Solenoids, DC Motors, Stepper Motors, Servo Motors Linear Actuators Pneumatic Muscles Shape Memory Alloys Weeks 5-6: Feedback and Control Systems Biological Feedback Mechanisms Biomechatronic Feedback Mechanisms Proportional and Higher-Order
    [Show full text]
  • Biomechatronic Systems
    Biomechatronic Systems Unit 1: Introduction Mehdi Delrobaei Winter 2020 1 Mechatronics Source: Wikipedia 2 Biomechatronics • Biomechatronics = Mechatronics + Biology (Medicine) • Biomechatronics = Bioelectrics + Biomechanics • Biomechatronics =? Bionics, Biomimetics 3 Source: Ekso Bionics Course Outline Course Description This course will cover the interdisciplinary elements of biomechatronic systems engineering and provides insight into the diverse applications of current biomechatronic technologies. Most lectures incorporate examples of emerging research and development activities across the medical and engineering fields. Course Evaluation Assignments: 10 % Mid-Term Exam: 25 % Project: 25 % Final Exam: 40 % 4 Topics Week 1: Introduction to Biomechatronics Bio-mechanics, Bio-electrics, Bionics, and Bio-mechatronics Physiological and Bio-mechanical Systems The Human Factors: Stimulus, Sensing, and Actuation Nervous System Musculoskeletal System Safety and Ethical Aspects Week 2: Signal Processing Biomedical and Bioelectric Signals Signal Acquisition Amplifiers and Noise Time Domain Analysis Frequency Domain Analysis Practical Considerations 5 Topics Weeks 3-4: Sensors and Actuators Simple Sensors: Switches, Resistive, Capacitive, Inductive, Magnetic Sonar and Optical Sensors Inertial Measurement Units Temperature, Pressure, and Tactile Sensing Body-Surface Biopotential Electrodes Simple Actuators: Solenoids, DC Motors, Stepper Motors, Servo Motors Linear Actuators Pneumatic Muscles Shape Memory Alloys Weeks 5-6: Feedback
    [Show full text]
  • TED Talks and Dave Eggers's the Circle
    COPAS – Current Objectives of Postgraduate American Studies Issue 20.1 (2019) The Future of the Enhanced Self and Contemporary Science Fiction: TED Talks and Dave Eggers’s The Circle Loredana Filip ABSTRACT: This article sets out to examine the ways in which cultural and literary texts actively shape the discourse on human enhancement. First, it identifies the emergence of a “sense of wonder” (Sawyer 87) in TED talks that advance transhumanist ideals. Second, it investigates the critical and ethical potential of Dave Eggers’s The Circle (2013) to challenge the notion of a ‘post-bodied future.’ KEYWORDS: TED Talks; Human Enhancement; Transhumanism; Sense of wonder; The Circle; Affect of Aliveness The Future Survival of Humanity The twenty-first century has witnessed amazing advancements in science and technology, such as bionics, genetic engineering through CRISPR-Cas9, or the first robot-citizen. The notion of scientific progress has captured the imagination of futurists who begin to imagine a better future for humanity. This act of imagining the future is often associated with the genre of science fiction. Fredric Jameson traced the genre’s emergence back to the nineteenth century when the historical novel was replaced by SF as a “form which now registers some nascent sense of the future” (150), rather than the past. Whether utopian or dystopian, visions of the future continue to be a key feature of SF, attesting to its speculative and prophetic dimensions.1 And yet they also reflect the contemporary social reality, which gives them a critical edge (cf. Haraway 6). As Jameson argued, they force us to “restructure our experience of our own present” (151).
    [Show full text]
  • Current Challenges to the Clinical Translation of Brain Machine Interface Technology
    Provided for non-commercial research and educational use only. Not for reproduction, distribution or commercial use. This chapter was originally published in the book International Review of Neurobiology, Vol. 107 published by Elsevier, and the attached copy is provided by Elsevier for the author's benefit and for the benefit of the author's institution, for non- commercial research and educational use including without limitation use in instruction at your institution, sending it to specific colleagues who know you, and providing a copy to your institution’s administrator. All other uses, reproduction and distribution, including without limitation commercial reprints, selling or licensing copies or access, or posting on open internet sites, your personal or institution’s website or repository, are prohibited. For exceptions, permission may be sought for such use through Elsevier's permissions site at: http://www.elsevier.com/locate/permissionusematerial From: Charles W. Lu, Parag G. Patil and Cynthia A. Chestek, Current Challenges to the Clinical Translation of Brain Machine Interface Technology. In Clement Hamani and Elena Moro, editors: International Review of Neurobiology, Vol. 107, Burlington: Academic Press, 2012, pp. 137-160. ISBN: 978-0-12-404706-8 © Copyright 2012 Elsevier Inc. Academic Press Author's personal copy CHAPTER SEVEN Current Challenges to the Clinical Translation of Brain Machine Interface Technology Charles W. Lu*, Parag G. Patil*,†,1, Cynthia A. Chestek* *Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA †Department of Neurosurgery, University of Michigan Health System, Ann Arbor, Michigan, USA 1Corresponding author: e-mail address: [email protected] Contents 1. Introduction and Motivation 137 2.
    [Show full text]