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Human-Computer Interaction

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Title: Human-Computer Interaction Author: HARSHA Last modified by: INTEL Created Date: 11/3/2010 3:23:46 PM Document presentation format: On-screen Show (4:3) – PowerPoint PPT presentation

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Title: Human-Computer Interaction


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An EEG-based Humanoid Robot Control System
with a Brain Machine Interface
  • P. Suma Priya M. Uma Maheswari
  • III B.Tech III B.Tech
  • CSE CSE

3

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ABSTRACT
  • Provides ability to communicate and gain control
    over environment to disabled or Locked in
    patients
  • Brain Computer Interface(BCI)

5
  • EEG signals or Brain waves
  • Only 2-3 tasks/minute.
  • This paper includes a discussion of pros and cons
    of an EEG-based user interfaces.

6
Introduction
  • People who are paralyzed need alternative methods
    for communicate and control. But, currently..
  • The severe motor disabilities people express
    their wishes only through their Brains.
  • EEG signals acts as a communication between men
    and machines
  • Signal processing is crucial in development of a
    real-time BCI
  • The goal of creating more effective algorithms
    and the investigations in search of new
    techniques of feature extraction.

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  • Neural Networks are used for pattern recognition
    and pattern generation.
  • Pattern-oriented techniques involved are signal
    processing, system identification, optimization,
    and control theory.

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Brain-Computer Interface
  • Uses
  • Bioengineering applications
  • Human subject monitoring
  • Neuroscience research
  • Man-Machine Interaction

9
  • People speculated that Electro Encephalo
    Graphic(EEG) activity or other measures of brain
    function might provide this new channel.
  • BCI research programs have began and encouraged
    new understanding of brain functions
  • Immediate goal is to provide communication
    capabilities so that any subject can control the
    external world without using brains normal
    output pathways of peripheral nerves and muscles.

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  • Nowadays, such activities drive their efforts in
  • Brain (neural) signal acquisition
  • Algorithms and processing
  • Underlying neuroscience

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  • BCIs use EEG activity recorded at the scalp
  • Most important is the central element
  • BCI operation depends on effective interaction
    between two adaptive controllers.
  • Current BCIs have maximum information transfer
    rates of 5-25 bits/min.

12
Achievement of greater speed and accuracy depends
on improvements in
  • Signal acquisition
  • Single trial analysis
  • Co-learning
  • Experimental paradigms for interpretable readable
    signals
  • Understanding algorithms and models within the
    context of the neurobiology

13
STRUCTURE OF BCI
  • Signal Acquisition
  • Signal Pre-Processing
  • conversion of analog-to-digital data at the rate
    of 100-300 Hz per channel

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HCI Systems Architecture
  • Signal Classification
  • In order to find out what the user wants, a BCI
    system has to classify the preprocessed data.
  • Does not understand users intentions, but it
    compares with limited number of classes, and
    selects the best one.
  • Computer Interaction

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REAL TIME APPLICATIONS FOR HUMONOID ROBOTICS
  • Objective is to control humanoid robot with our
    brain directly

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PROCESS STEPS
  • Input
  • Input signals are amplified
  • Analyzing
  • measure, analyze and controls the
    robot using real-time OS
  • Functioning
  • The robot moves arm, legs and more by result of
    analysis that from EEG signals.

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Analyzing EEG Signals
  • Fractal-Dimension analysis are used for analyzing
    EEG signals which is advantageous compared to
    Frequency analysis signals.

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  • Features of BMI
  • Brain computer interface is a man-machine
    interface which can control machines without
    hands or legs.
  • It only uses EEG signals.
  • It is the next generation interfaces which
    connect directly to the brain.
  • Applications
  • Artificial arm and leg
  • Special assistance system
  • communication system
  • Problem
  • The systems order is only 2 patterns

22
Present and Future
  • The prospects for controlling computers through
    neural signals are indeed difficult to judge
    because the field of research is still in its
    infancy.
  • The research is still continues for the advanced
    developments
  • One day perhaps newly purchased computer will
    arrive with biological signal sensors and
    thought-recognition software built in.

23
Conclusions
  • Recent developments are communication between
    the brain and machine.
  • Increases the applicability of neuroscience and
    neurotechnology.
  • Developing new algorithms to decode the signals
    from the individual brains.
  • In future BMI/BCI research will bring major
    advances in brain science and information
    technology

24
  • The WORLD going to see a programmers,
    software designers and computer experts who cant
    able to write their own program!!!!!!!!

25
QURIES
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