Neuroprosthetics *= Hecke. CNS-Seminar 2004 Opener p.1

Similar documents
Real Robots Controlled by Brain Signals - A BMI Approach

Predicting 3-Dimensional Arm Trajectories from the Activity of Cortical Neurons for Use in Neural Prosthetics

I+ I. Eric Eisenstadt, Ph.D. DARPA Defense Sciences Office. Direct Brain-Machine Interface. Science and Technology Symposium April 2004

Presented by: V.Lakshana Regd. No.: Information Technology CET, Bhubaneswar

Design and implementation of brain controlled wheelchair

Brain Computer Interfaces for Full Body Movement and Embodiment. Intelligent Robotics Seminar Kai Brusch

Motor Imagery based Brain Computer Interface (BCI) using Artificial Neural Network Classifiers

BRAIN COMPUTER INTERFACES FOR MEDICAL APPLICATIONS

The computational brain (or why studying the brain with math is cool )

The Data: Multi-cell Recordings

Mobile robot control based on noninvasive brain-computer interface using hierarchical classifier of imagined motor commands

Carnegie Mellon University!!

MIND OVER METAL. The cyborgs are here and they're helping us understand how the brain works. Anil Ananthaswamy investigates

Eur Ing Dr. Lei Zhang Faculty of Engineering and Applied Science University of Regina Canada

Research Seminar. Stefano CARRINO fr.ch

Maps in the Brain Introduction

Artificial Intelligence and the Singularity

Real-Time Decoding of an Integrate and Fire Encoder

BRAIN CONTROLLED CAR FOR DISABLED USING ARTIFICIAL INTELLIGENCE

Brain-Machine Interface for Neural Prosthesis:

Brain Computer Interface Control of a Virtual Robotic System based on SSVEP and EEG Signal

Classifying the Brain's Motor Activity via Deep Learning

EE105 Fall 2015 Microelectronic Devices and Circuits Multi-Stage Amplifiers. Prof. Ming C. Wu 511 Sutardja Dai Hall (SDH)

Fundamentals of Computer Vision

Breaking the Wall of Neurological Disorder. How Brain-Waves Can Steer Prosthetics.

Sensing self motion. Key points: Why robots need self-sensing Sensors for proprioception in biological systems in robot systems

Robots: Tools or Toys? Some Answers from Biorobotics, Developmental and Entertainment Robotics. AI and Robots. A History of Robots in AI

Smart Phone Accelerometer Sensor Based Wireless Robot for Physically Disabled People

A wireless neural recording system with a precision motorized microdrive for freely

BLUE BRAIN - The name of the world s first virtual brain. That means a machine that can function as human brain.

HUMAN COMPUTER INTERACTION

Development of a Laboratory Kit for Robotics Engineering Education

Preprocessing & Feature Extraction in Signal Processing Applications

Biomedical and Wireless Technologies for Pervasive Healthcare

Coordinate system representations of movement direction in the premotor cortex

Bi-directional brain computer interface with brain implantable Arm-based SoCs Joseph Fernando Principal architect

Motivated Copter. ( Brain-controlled drone ) Arash Molavi Deep Singh Girish Pawar Guide: Prof. Guevara Noubir

Motor Cortical Representation of Hand Translation and Rotation during Reaching

A SEMINAR REPORT ON BRAIN CONTROLLED CAR USING ARTIFICIAL INTELLIGENCE

Virtual Grasping Using a Data Glove

SpiNNaker SPIKING NEURAL NETWORK ARCHITECTURE MAX BROWN NICK BARLOW

An Overview of Brain-Computer Interface Technology Applications in Robotics

The Neuronal Basis of Visual Self-motion Estimation

COS Lecture 7 Autonomous Robot Navigation

Visual Interpretation of Hand Gestures as a Practical Interface Modality

Lecture VII. CNS Patterning. Reading

Neural Network Application in Robotics

Neural control of computer cursor velocity by decoding motor. cortical spiking activity in humans with tetraplegia

A multi-channel telemetry system for brain microstimulation in freely roaming animals

Bio-Signal Based Human-Computer Interface for Geometric. Modeling

A Framework for Assessing the Feasibility of Learning Algorithms in Power-Constrained ASICs

LASA I PRESS KIT lasa.epfl.ch I EPFL-STI-IMT-LASA Station 9 I CH 1015, Lausanne, Switzerland

Human-to-Human Interface

SUPPLEMENTARY MATERIAL. Technical Report: A High-Performance Neural Prosthesis Enabled by Control Algorithm Design

Artificial Intelligence: An overview

MINE 432 Industrial Automation and Robotics

A.I in Automotive? Why and When.

Non Invasive Brain Computer Interface for Movement Control

I.1 Smart Machines. Unit Overview:

cogs1 mapping space in the brain Douglas Nitz April 30, 2013

* Intelli Robotic Wheel Chair for Specialty Operations & Physically Challenged

On Intelligence Jeff Hawkins

For Experimenters and Educators

GPU Computing for Cognitive Robotics

A Comprehensive Study of Artificial Neural Networks

Design of a Bionic Hand Using Non Invasive Interface

NCCT IEEE PROJECTS ADVANCED ROBOTICS SOLUTIONS. Latest Projects, in various Domains. Promise for the Best Projects

TJHSST Senior Research Project Evolving Motor Techniques for Artificial Life

Mobile Target Tracking Using Radio Sensor Network

Synthetic Brains: Update

Non-Invasive Brain-Actuated Control of a Mobile Robot

A Hardware Design for In-Brain Neural Spike Sorting

Brain-Computer Interface for Control and Communication with Smart Mobile Applications

Haptic Discrimination of Perturbing Fields and Object Boundaries

ISONIC PA AUT Spiral Scan Inspection of Tubular Parts Operating Manual and Inspection Procedure Rev 1.00 Sonotron NDT

InteligĂȘncia Artificial. Arlindo Oliveira

Arati Prabhakar, former director, Defense Advanced Research Projects Agency and board member, Pew Research Center: It s great to be here.

The Somatosensory System. Structure and function

II. LITERATURE REVIEW

Important Tools and Perspectives for the Future of AI

In vivo Performance Evaluation of Implantable Wireless Neural Signal Transmission System for Brain Machine Interface

Mobile Target Tracking Using Radio Sensor Network

! The architecture of the robot control system! Also maybe some aspects of its body/motors/sensors

An Exploration of the Utilization of Electroencephalography and Neural Nets to Control Robots

An autonomous implantable computer for neural recording and stimulation in unrestrained primates

Evolving Robot Empathy through the Generation of Artificial Pain in an Adaptive Self-Awareness Framework for Human-Robot Collaborative Tasks

By Marek Perkowski ECE Seminar, Friday January 26, 2001

BRAINWAVE RECOGNITION

Birth of An Intelligent Humanoid Robot in Singapore

R (2) Controlling System Application with hands by identifying movements through Camera

Research on Image Processing System for Retinal Prosthesis

Emoto-bot Demonstration Control System

Kinect Interface for UC-win/Road: Application to Tele-operation of Small Robots

Toward Video-Guided Robot Behaviors

Intelligent Robot Based on Synaptic Plasticity and Neural Networks

Haptic Perception & Human Response to Vibrations

Non-Invasive Brain-Actuated Control of a Mobile Robot

Spatial navigation in humans

Smart Manufacturing: A Big Data Perspective. Andrew Kusiak Intelligent Systems Laboratory The University of Iowa Iowa City, Iowa USA

WHEELCHAIR MOVEMENT CONTROL USING TONGUE DRIVEN WIRELESS ASSISTIVE TECHNOLOGY

Transcription:

Neuroprosthetics *= *. Hecke MPI für Dingsbums Göttingen CNS-Seminar 2004 Opener p.1

Overview 1. Introduction CNS-Seminar 2004 Opener p.2

Overview 1. Introduction 2. Existing Neuroprosthetics CNS-Seminar 2004 Opener p.2

Overview 1. Introduction 2. Existing Neuroprosthetics 3. Research areas CNS-Seminar 2004 Opener p.2

Overview 1. Introduction 2. Existing Neuroprosthetics 3. Research areas 4. Experiments CNS-Seminar 2004 Opener p.2

Overview 1. Introduction 2. Existing Neuroprosthetics 3. Research areas 4. Experiments 5. Outlook CNS-Seminar 2004 Opener p.2

Introduction Five principles for the design of Neuroprosthetics: Nicolelis, M. A. L. (2003) Brain-machine interfaces to restore motor function and probe neural circuits. CNS-Seminar 2004 Opener p.3

Introduction Five principles for the design of Neuroprosthetics: Motor information is distributedly represented Nicolelis, M. A. L. (2003) Brain-machine interfaces to restore motor function and probe neural circuits. CNS-Seminar 2004 Opener p.3

Introduction Five principles for the design of Neuroprosthetics: Motor information is distributedly represented Multiple motor parameters can be extracted in real time Nicolelis, M. A. L. (2003) Brain-machine interfaces to restore motor function and probe neural circuits. CNS-Seminar 2004 Opener p.3

Introduction Five principles for the design of Neuroprosthetics: Motor information is distributedly represented Multiple motor parameters can be extracted in real time Therefore recording a few hundred neurons is sufficient Nicolelis, M. A. L. (2003) Brain-machine interfaces to restore motor function and probe neural circuits. CNS-Seminar 2004 Opener p.3

Introduction Five principles for the design of Neuroprosthetics: Motor information is distributedly represented Multiple motor parameters can be extracted in real time Therefore recording a few hundred neurons is sufficient It has to be taken advantage of plasticity of the structure Nicolelis, M. A. L. (2003) Brain-machine interfaces to restore motor function and probe neural circuits. CNS-Seminar 2004 Opener p.3

Introduction Five principles for the design of Neuroprosthetics: Motor information is distributedly represented Multiple motor parameters can be extracted in real time Therefore recording a few hundred neurons is sufficient It has to be taken advantage of plasticity of the structure Different patterns can encode the same movement Nicolelis, M. A. L. (2003) Brain-machine interfaces to restore motor function and probe neural circuits. CNS-Seminar 2004 Opener p.3

Example for NP Cochlear implants: CNS-Seminar 2004 Opener p.4

Research areas: measuring The first step is to extract information out of the brain. Black J.B., Serruya M., Bienenstock E., Gao Y., Wu W., Donoghue J. P. (2003) Connecting Brains with machines: The neural Control of 2D Cursor Movement. CNS-Seminar 2004 Opener p.5

Research areas: measuring The first step is to extract information out of the brain. Black J.B., Serruya M., Bienenstock E., Gao Y., Wu W., Donoghue J. P. (2003) Connecting Brains with machines: The neural Control of 2D Cursor Movement. CNS-Seminar 2004 Opener p.5

coding and decoding How is Information represented in the brain? CNS-Seminar 2004 Opener p.6

coding and decoding How is Information represented in the brain? Which algorithms can be used to decode the signals? CNS-Seminar 2004 Opener p.6

coding and decoding How is Information represented in the brain? Which algorithms can be used to decode the signals? ANN s: don t care about the representation CNS-Seminar 2004 Opener p.6

coding and decoding How is Information represented in the brain? Which algorithms can be used to decode the signals? ANN s: don t care about the representation Kalman Filters: benefit from the linear dependence of the firing rate to hand motion direction and to position and acceleration. CNS-Seminar 2004 Opener p.6

actual questions User friendlyness of interfaces - as the pair hand movement - mouse pointer CNS-Seminar 2004 Opener p.7

actual questions User friendlyness of interfaces - as the pair hand movement - mouse pointer 3D-control for artificial limbs CNS-Seminar 2004 Opener p.7

actual questions User friendlyness of interfaces - as the pair hand movement - mouse pointer 3D-control for artificial limbs Mobile robot platform for wheelchairs CNS-Seminar 2004 Opener p.7

actual questions User friendlyness of interfaces - as the pair hand movement - mouse pointer 3D-control for artificial limbs Mobile robot platform for wheelchairs How many electrodes are sufficent for good control? CNS-Seminar 2004 Opener p.7

actual questions User friendlyness of interfaces - as the pair hand movement - mouse pointer 3D-control for artificial limbs Mobile robot platform for wheelchairs How many electrodes are sufficent for good control? Implants in humans? CNS-Seminar 2004 Opener p.7

Ex 1: Reaching and grasping adaptation to direct cursor control fast adaptation to the dynamics of the robotic arm -> simulation CNS-Seminar 2004 Opener p.8

perfor- Prediction mance CNS-Seminar 2004 Opener p.9

Experiments 2 Serruya et al. (2002) Instant neural control of a movement signal. CNS-Seminar 2004 Opener p.10

Exp 3: Remote controlled rat Provide control signals via microelectrodes to the primary somatosensory cortex Talwar et al. (2002) Rat navigation guided by remote control. Nicolelis M. A. L. (2002) The amazing adventures of Robotrat. CNS-Seminar 2004 Opener p.11

Exp 3: Remote controlled rat Provide control signals via microelectrodes to the primary somatosensory cortex either to the left or the right whisker representation, meaning "Turn left!" or "Turn right" Talwar et al. (2002) Rat navigation guided by remote control. Nicolelis M. A. L. (2002) The amazing adventures of Robotrat. CNS-Seminar 2004 Opener p.11

Exp 3: Remote controlled rat Provide control signals via microelectrodes to the primary somatosensory cortex either to the left or the right whisker representation, meaning "Turn left!" or "Turn right" electrical stimulus in the medial forebrain bundle as reward for learning Talwar et al. (2002) Rat navigation guided by remote control. Nicolelis M. A. L. (2002) The amazing adventures of Robotrat. CNS-Seminar 2004 Opener p.11

Exp 3: Remote controlled rat Provide control signals via microelectrodes to the primary somatosensory cortex either to the left or the right whisker representation, meaning "Turn left!" or "Turn right" electrical stimulus in the medial forebrain bundle as reward for learning locate individuals for rescuing Applications: Feedback of Neuroprosthetics to the nervous system Talwar et al. (2002) Rat navigation guided by remote control. Nicolelis M. A. L. (2002) The amazing adventures of Robotrat. CNS-Seminar 2004 Opener p.11

Experiments 4 -> Video by Andrew Schwartz CNS-Seminar 2004 Opener p.12

Outlook Silver J. (1999) The Matrix. CNS-Seminar 2004 Opener p.13