Download bio-control system

CHAPTER-1
INTRODUCTION
The video and thermo gram analyzer continuously monitor activities outside the
car. A brain-computer interface (BCI), sometimes called a direct neural interface or a
brain-machine interface, is a direct communication pathway between a human or animal
brain (or brain cell culture) and an external device. In one-way BCIs, computers either
accept commands from the brain or send signals to it (for example, to restore vision) but
not both. Two-way BCIs would allow brains and external devices to exchange
information in both directions but have yet to be successfully implanted in animals or
humans. In this definition, the word brain means the brain or nervous system of an
organic life form rather than the mind. Computer means any processing or
computational device, from simple circuits to silicon chips (including hypothetical
future technologies such as quantum computing).
Figure 1.1 Brain Controlled Car
Once the driver (disabled) nears the car. The security system of the car is
activated. Images as well as thermo graphic results of the driver are previously fed into
the database of the computer. If the video images match with the database entries then
the security system advances to the next stage. Here the thermo graphic image
verification is done with the database.
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After the driver passes this stage the door slides to the sides and a ramp is
lowered from its floor. The ramp has flip actuators in its lower end. Once the driver
enters the ramp, the flip actuates the ramp to be lifted horizontally as shown in
Figure 1.1. Then robotic arms assist the driver to his seat. As soon as the driver is seated
the EEG (electroencephalogram) helmet, attached to the top of the seat, is lowered and
suitably placed on the driver’s head as shown in the Figure1.2.
Figure1.2 : EEG Helmet
A wide screen of the computer is placed at an angle aesthetically suitable to the
driver. Each program can be controlled either directly by a mouse or by a shortcut. For
starting the car, the start button is clicked. Accordingly the computer switches ON the
circuit from the battery to the A.C Series Induction motors.
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CHAPTER-2
BIO-CONTROL SYSTEM
The Biocontrol system integrates signals from various other systems and
compares them with originals in the database. It comprises of the following systems:

Brain-computer interface

Automatic security system

Automatic navigation system
Now let us discuss each system in detail.
2.1 Brain – Computer interface:
Brain computer interface It sometimes called a direct neural interface or a brainmachine. It is a direct communication pathway between a human and an external device.
Two-way BCIs would allow brains and external devices to exchange information in
both directions.
Brain-computer interfaces will increase acceptance by offering customized,
intelligent help and training, especially for the non-expert user. Development of such a
flexible interface paradigm raises several challenges in the areas of machine perception
and automatic explanation. The teams doing research in this field have developed a
single-position, brain-controlled switch that responds to specific patterns detected in
spatiotemporal electroencephalograms (EEG) measured from the human scalp. We refer
to this initial design as the Low-Frequency Asynchronous Switch Design (LF-ASD) as
shown in Figure 2.1.
Figure 2.1 LF-ASD
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The EEG is then filtered and run through a fast Fourier transform before being
displayed as a three dimensional graphic. The data can then be piped into MIDI
compatible music programs. Furthermore, MIDI can be adjusted to control other
external processes, such as robotics. The experimental control system is configured for
the particular task being used in the evaluation. Real Time Workshop generates all the
control programs from Simulink models and C/C++ using MS Visual C++ 6.0. Analysis
of data is mostly done within Mat lab environment.
2.1.1 EEG Transmission:
EEG Transmission EEG (electroencephalogram) A wide screen of the computer
is placed at an angle aesthetically suitable to the driver. Each program can be
controlled either directly by a mouse or by a short cut. The EEG transmission is
shown in the Figure 2.2.
Figure 2.2 EEG Transmission
2.1.2 Features of EEG band
 Remote analysis data can be sent and analyzed in real-time over a network or
modem
connection
 Data can be fully exported in raw data, FFT & average formats. Ultra Low noise
balanced DC coupling amplifier.
 Max input 100microV p-p, minimum digital resolution is 100 micro V p-p / 256 =
0.390625 micro V p-p. FFT point can select from 128 (0.9375 Hz), 256 (0.46875
Hz), 512(0.234375 Hz resolution).
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 Support for additional serial ports via plug-in boar; allows extensive serial input &
output control.
 Infinite real-time data acquisition (dependent upon hard drive size).
 Real-time 3-D & 2-D FFT with peak indicator, Raw Data, and Horizontal Bar
displays with Quick Draw mode.
 Full 24 bit color support; data can be analyzed with any standard or user.
 Customized color palettes; color cycling available in 8 bit mode with Quick Draw
mode.
 Interactive real-time FFT filtering with Quick Draw mode. Real-time 3-D FFT (left,
right, coherence and relative coherence), raw wave, sphere frequency and six brain
wave switch in one OpenGL display.
 Full Brainwave driven Quick Time Movie, Quick Time MIDI control; user
configurable
 Full Brain wave driven sound control, support for 16 bit sound; user configurable
 Full image capture and playback control; user configurable.
Table 2.1 Sequence of operations that convert Scalp EEG Activity into Cursor
Movement
Device
Input
Operation
Output
EEG amplifier
Scalp voltages(µV)
Amplification
Analog voltages(V)
A/D board
Analog Voltages
Digitization
Digitized voltages
DSP board
Digitized voltages
Spatial filtering
EEG cursor-control
channels
DSP board
EEG cursor-control
Spectral analysis
Control signals
Transformation
Cursor movements
channels
PC
Control signals
22.1.3 Test results Comparing driver accuracy with/without BCI:
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 Able-bodied subjects using imaginary movements could attain equal or better
control accuracies than able-bodied subjects using real movements.
 Subjects demonstrated activation accuracies in the range of 70-82% with false
activations below 2%.
 Accuracies using actual finger movements were observed in the range 36-83%.
 The average classification accuracy of imaginary movements was over 99%.
2.1.4 Brain to Machine Interface:
Figure 2.4 Brain-to- Machine Mechanism
The block diagram for Brain to Machine Interface is shown in the above Figure
2.4. The principle behind the whole mechanism is that the impulse of the human brain
can be tracked and even decoded. The Low-Frequency Asynchronous Switch Design
traces the motor neurons in the brain.
When the driver attempts for a physical movement, he/she sends an impulse to
the motor neuron. These motor neurons carry the signal to the physical components
such as hands or legs. Hence we decode the message at the motor neuron to obtain
maximum accuracy. By observing the sensory neurons we can monitor the eye
movement of the driver.
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Figure 2.5 Eyeball Tracking
The eye ball Tracking is shown in the above Figure 2.5. As the eye moves, the
cursor on the screen also moves and is also brightened when the driver concentrates on
one particular point in his environment. The sensors, which are placed at the front and
rear ends of the car, send a live feedback of the environment to the computer. The
steering wheel is turned through a specific angle by electro mechanical actuators. The
angle of turn is calibrated from the distance moved by the dot on the screen.
The Electro mechanical Control Uint is shown in the Figure2.6
Figure 2.6 Electro mechanical Control Unit
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Figure 2.7 Sensors and Their Range
2.2 Automatic Security System:
The EEG of the driver is monitored continually. When it drops less than 4 Hz
then the driver is in an unstable state. A message is given to the driver for confirmation
to continue the drive. A confirmed reply activates the program automatic drive. The
computer prompts the driver for the destination before the drive.
2.3 Automatic Navigation System:
As the computer is based on artificial intelligence it automatically monitors
every route the car travels and stores it in its map database for future use. The map
database is analyzed and the shortest route to the destination is chosen. With traffic
monitoring system provided by xm satellite radio the computer drives the car
automatically. Video and anticollision sensors mainly assist this drive by providing
continuous live feed of the environment up to 180 m, which is sufficient for the purpose.
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CHAPTER-3
WORKING OF EEG
EEG is the recording of electrical activity in the brain. The activity is recorded
by placing electrodes on the skull over electrically active tissue and measuring the
voltage across these electrodes. The electrical activity arises from the firing of many
neurons in the brain. There is always a “background” firing of neurons, although the
pattern changes when the subject under study is active. One area of interest is the study
of waves: these are oscillations in the EEG that are grouped into four different
frequency bands:
Figure 3.1 : Time Series plot of EEG data
Alpha waves have frequencies ranging from 8 to 13Hz. They are usually present in a
normal human at rest with closed eyes and not subjected to any external stimulus. They
can be blocked by stimulation or external activity.
Beta waves have frequencies ranging from 14 to 30Hz and mainly occur when the
subject is exposed to an external stimulus; they are not as strongly periodic as the alpha
waves.
Theta waves have frequencies ranging from 3 to 8Hz. They are present in the EEG of
new borns or adults who have disease or injury.
Delta waves are as theta waves except their frequency range is 0.5-3.5Hz.
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3.1 DESIGN:
Driving the vehicle is only one of the many tasks drivers need to focus on. As
such, a BCI powered interaction method could relieve the cognitive workload of the
driver and reduce the level of distractions introduced by manually controlling multiple
in-vehicle systems.
Figure3.2 Block Diagram of EEG
Description of EEG Control
Figure 3.3 shows scalp topographies generated off-line from 64 channels of EEG
data collected during performance of one-dimensional cursor control by one well trained
user. The control signal was the sum of the voltages at 10Hz at two locations centered
over right and left sensorimotor cortices, respectively. Figure 3.3A shows topographies
of the mean 10-Hz voltage computed when the target was at the top or bottom edge of
the screen. Mean 10-Hz voltages over both sensorimotor cortices are much greater
during top targets than during bottom targets. Voltage is highest on the right side. Figure
3.3B shows the 10-Hz topography of y2. This measure can be considered an index of the
signal-to-noise ratio at each location. Like the voltage difference evident in A, the value
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of y2 is greatest over the sensorimotor cortices. However, in contrast to A, the largest
value of y2 is over the left side. These topographical displays illustrate the typically
narrow spatial focus of a well-trained user's EEG control.
Figure 3.3 Topographical analysis of four sessions from a well-trained user
(A) Average voltages at 10 Hz for top and bottom targets.
(B) Values of r 2 for the top/bottom difference.
3.2 Controlling In-Vehicle Systems with a Commercial EEG Headset
During the experiment, the participants were involved in executing a set of
activities that are always or at least sometimes present in everyday driving. The
activities were grouped in the following three categories:
 Driving task (highest priority) – firstly, every driver has to be in control of the
vehicle and obey the traffic rules.
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 Search task (high priority) – drivers are sometimes involved in search-and-find
tasks in the environment surrounding the vehicle (e.g., finding a parking lot, finding
a store, etc.)
 Control task (low priority) – drivers have to interact with the dashboard of the
vehicle (in our case via BCI) to activate various devices and vehicle subsystems
(e.g., lights, radio, GPS, etc.)
Figure 3.4 Development of EEG control following the appearance of target
Figure 3.4 illustrates, with data from a well-trained user, the timing of the
development of EEG control in response to appearance of a top or bottom target. It plots
the control signal (i.e., 10-Hz voltage over left sensorimotor cortex) that controlled
cursor movement on-line. Average values are shown for every 100-msec time segment
from the first appearance of the target until the cursor started moving. It is clear that the
user's response to the target develops within the first second after the appearance of the
cursor. This information is important for efforts to maximize the rate and accuracy of
cursor control by modifying trial timing.
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CHAPTER-4
CONCLUSION
When the above requirements are satisfied and if this car becomes cost effective
then hope that revolutionary change in the society where the demarcation between the
abler and the disabled vanishes. Thus the integration of bioelectronics with automotive
systems is essential to develop efficient and futuristic vehicles, which shall be witnessed
soon helping the disabled in every manner in the field of transportation.
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References:
[1]
Flotzinger, D. Kalcher, J.Wolpaw, J.R., McFarland, J.J. and Pfurtscheller, ”Off-line
Classification of EEG from the New York Brain-Computer Interface (BCI)"
Graz University of Technology, Austria 1993.
[2]
Keirn, Z.A. and Aunon, J.I.," Man-Machine
Communications
through
Brain-
Wave Processing IEEE Engineering in Medicine and Biology Magazine, March 1990.
[3]
Sutter, E.E., "The brain response interface: communication through visually-induced
electrical brain responses" Journal of Microcomputer Applications, 1992, 15: 31-45.
[4]
www.seminarsforyou.com
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