artificial prothetic limbs

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  • Artificial prosthetic limbsProblems and solutions for connecting brains

    and robots

    Advanced Seminar

    submitted byConstantin UhdeNicolas Berberich

    NEUROSCIENTIFIC SYSTEM THEORYTechnische Universitat Munchen

    Prof. Dr Jorg Conradt

    Supervisor: Viviane Ghaderi, Ph.D.Final Submission: 07.07.2015

  • Abstract

    Current motorized limb prostheses provide rudimentary functionality for the appli-cation in every day life. Together with poor cosmetic appearance this is the reasonwhy a large percentage of amputees do not use their prosthetic device regularly. Thisseminar paper seeks to present an overview of current state of the art research onneural interfaces. The focus lies on non-invasive recording with EMG and especiallyHigh Density EMG sensors. Additionally, different machine learning and patternrecognition algorithms for the decoding of the recorded signals are discussed. Finally,promising research directions for advanced prosthesis control will be discussed.

  • 2

  • CONTENTS 3

    Contents

    1 Introduction 5

    2 State of the Art of BCI technology 72.1 BCI Input Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . 7

    2.1.1 Invasive Brain Recording . . . . . . . . . . . . . . . . . . . . . 72.1.2 Noninvasive Brain Recording . . . . . . . . . . . . . . . . . . . 92.1.3 Indirect Brain Recording . . . . . . . . . . . . . . . . . . . . . 102.1.4 Supplementary Technologies . . . . . . . . . . . . . . . . . . . 12

    2.2 BCI Output Technologies . . . . . . . . . . . . . . . . . . . . . . . . 132.2.1 Prosthetic Limbs . . . . . . . . . . . . . . . . . . . . . . . . . 132.2.2 Robotic Arms . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2.3 Exoskeletons . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

    3 EMG Method 173.1 Signal source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.2 Electrodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.3 Recording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

    3.3.1 Signal contamination . . . . . . . . . . . . . . . . . . . . . . . 193.4 High density EMG . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

    3.4.1 Hardware requirements . . . . . . . . . . . . . . . . . . . . . . 203.4.2 Curse of dimensionality . . . . . . . . . . . . . . . . . . . . . . 213.4.3 Detection of bad electrode signals . . . . . . . . . . . . . . . . 22

    4 Decoding Algorithms for EMG-based Prostheses 234.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244.3 Machine learning approaches . . . . . . . . . . . . . . . . . . . . . . . 26

    4.3.1 Standard Algorithms . . . . . . . . . . . . . . . . . . . . . . . 264.3.2 Neuro-inspired Algorithms . . . . . . . . . . . . . . . . . . . . 27

    4.4 Pattern Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

    5 Summary and Outlook 315.1 Advances and Problems . . . . . . . . . . . . . . . . . . . . . . . . . 315.2 Promising Research Areas . . . . . . . . . . . . . . . . . . . . . . . . 31

  • 4 CONTENTS

    5.2.1 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 315.2.2 Bidirectional closed-loop neuroprostheses . . . . . . . . . . . . 325.2.3 Multi-modal Approaches . . . . . . . . . . . . . . . . . . . . . 325.2.4 Semi-Autonomous Control . . . . . . . . . . . . . . . . . . . . 33

    List of Figures 35

    Bibliography 37

  • 5

    Chapter 1

    Introduction

    The first part of this composition gives an overview of current technological ap-proaches for interfacing brains and computers, so called brain-computer-interfaces(BCIs). This is a very general term, because the brain side of the interface canmean getting the data directly by measuring brain functions but it can also mean,that the cognitive activity is measured indirectly by muscle activation or other cues.Likewise, the computer part of the interface can stand for a standard personalcomputer, a robotic manipulator or a prosthetic device like a cochlea implant or anartificial limb. In this report we focus mainly on the non-invasive EMG technologyfor the input and an artificial limb for the output. While we believe, that there isno clear best BCI, we do think that there are technologies that perform better orbest for certain fields of applications. As the main concern of this report is machinesthat can replace a missing limb, we will take a deeper look into artificial limbs thesecond part of chapter 2 and into the EMG technology in chapter 3, with a focuson High Density EMG. We intend to show why neuroprosthetic systems that arebased on those two technologies are very promising in devices that amputees needin terms of functionality and control but also flexibility and appearance.In chapter 4 we examine the methods that are used to process the EMG data, decodeit and infer meaning by means of machine learning algorithms and send control com-mands to the artificial limb. Different algorithms, both classic and neuro-inspired,will be presented and compared in order to achieve a better understanding of thismost critical part of modern BCIs.The last part of this report will summarize the achievements of EMG-based neuro-prosthetic technology as well as its most dominant problems. Based on this analysisa short outlook on future research will be presented.

  • 6 CHAPTER 1. INTRODUCTION

  • 7

    Chapter 2

    State of the Art of BCI technology

    This chapter seeks to summarize current state of the art technology that is beingused for brain-computer-interfaces and robotic limb prostheses.

    2.1 BCI Input Technologies

    There is a multitude of methods for interfacing brains and computers that havespecific strengths and weaknesses and are thus used for many different applications.Since the topic of this seminar paper is the use of BCIs for the control of prostheticlimbs, the most important criteria that the technologies have to be evaluated againstare real-time applicability, high precision, flexibility and adaptability as well as theability to control multiple degrees of freedom intuitively.

    2.1.1 Invasive Brain Recording

    The best way to acquire high quality signals of the neural activity is to surgicallyplace electrodes on the surface of the brain below the skull. However those ap-proaches are dangerous for the patient not only because they involve break of skinbut also due to the body rejecting a foreign object that can lead to infection.One method is Electrocorticography (ECoG), also called intracranial EEG (iEEG),where electrodes are placed directly on the brain surface to record the activity of thecerebral cortex. It was initially developed at the Montreal Neurological Institute inthe 1950s to treat patients with epilepsy to identify the regions of the cortex thatgenerate the epileptic seizures. The signals that are measured by the extracellularmicro-electrodes are the local field potentials (LFPs) which are the sum of the syn-chronized postsynaptic voltages produced by multiple neurons in the vicinity of thechip. This leads to a spatial resolution of 1 cm and a temporal resolution of ap-proximately 5 ms [AJS+05]. If depth electrodes are used in conjunction with a highsampling rate (more than 10 kHz) it is possible to measure the action potentialsof individual neurons. The reason for this massive increase in resolution is, thatthe potentials are primarily produced by cortical pyramidal cells which lie several

  • 8 CHAPTER 2. STATE OF THE ART OF BCI TECHNOLOGY

    layers below the surface of the cortex. Using ECoG, low voltage, high frequencycomponents can be detected, that cannot be seen in scalp EEG.The most notable research project in area of invasive neural interfaces is BrainGate[brab], which resulted in the BrainGate brain implant system that is currently testedin clinical trials.

    Figure 2.1: The BrainGate neural interface [braa]

    The BrainGate system consists of a sensor which is implanted directly onto thebrains motor cortex and uses 100 hair-thin gold microwire electrodes mounted on asmall silicon array to record the neural activity of individual neurons. Those signalsare then sent to a metallic pedestal which is embedded in the skull and from there toan external decoder device where they are interpreted and used to control a roboticarm [Pat09]. This setup has become well known for a project conducted in 2012[HBJ+12] where a subject with tetraplegia (a paralysis of all limbs and torso) whowas enrolled in the BrainGate2 pilot clinical trial, uses the system to steer a roboticarm precisely enough to pick up and drink from a bottle of coffee, see Figure 2.2.This was the first time, she was able to drink unaided in 15 years.The implant was a 4mm x 4mm, 96-channel microelectrode array, which was im-planted 5 years earlier in the dominant hand area of the motor cortex and enabledthe decoding of the local ensemble spiking signals. For the output device, a DLRLight Weight Robot III was used which enabled a robust finger position and graspingof the object by automated joint impedance control. This robotic arm system willbe further discussed in section 2.2.2. Despite the impressive results of the system, ithas several drawbacks. Most noticeable the external decoding device severely limitsthe patients mobility and thus partly counteracts the systems main goal of givingback autonomy to the patient . Furthermore the recorded data has to be compressedbefore it can be sent to the decoder resulting in loss of potentially useful information.The connector component that goes through the skin entails a high risk of infection.

  • 2.1. BCI INPUT TECHNOLOGIES 9

    Figure 2.2: A tetraplegic BrainGate2 clinical trial participant drinking from a

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