A brain-computer interface uses EEG signals measured from the brain to trigger specific actions by a microcontroller. EEG signals are measured from the brain using an electroencephalograph and analyzed to detect patterns associated with activities like blinking or moving body parts. These patterns in the EEG graphs can then trigger predetermined courses of action by the microcontroller, such as controlling devices to help those with disabilities. The document outlines the process of using brain waves detected by EEG to communicate with and control a microcontroller.

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2. Simulate a Microcontroller With the help of
Brain Wave Patterns To Trigger a Specific
Course of Action is done by BCI process.
A brain-computer interface (BCI), sometimes
Called a Direct neural interface or a brain-
machine interface, is a direct communication
pathway between a human and an external
device
 For these purpose , A
Electroencephalograph is chosen for
measuring the impedance of the brain waves
there by generating a EEG graph.
By Analyzing these EEG graph And Trigger a
specific course of action on a microcontroller
circuit.
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3.  By conducting experiments,
we found out that simple
activities like blinking of eye,
movement of legs, and
movement of arms produced a
specific wave in the brain. The
peaks in the waveform denote
the action, there by triggering
the course of action.
 In the long run, the triggering
could be used in highly
complicated circuits like an
advanced security system.
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17. Delta Wave Theta wave
Alpha wave Mu wave
Beta wave Gamma waves
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21. An Example of Playing an Game 21

22. Controlling a wheel chair
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26. Jens Naumann, a man with
acquired blindness, being
interviewed about his
vision BCI on CBS's The
Early Show
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27. User has a EEG cap on. By thinking about left and right
hand movement the user controls the virtual keyboard with
her brain activity.
Virtual Keyboards
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28. Advantages of processing EEG
signal using µC
• Reduce power consumption
• Reduce system implementation cost
• Reduce space
• Reduce maintenance
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29. Brain to Microcontroller
Interfacing
EEG acquisition
Filtering
Amplification
ADC
&
Sampling
Processing
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30. Circuit Diagram
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32. Interface
• Begins at surface electrode location
• Each electrode is attached to skin
• Electrode material will not interact
chemically with skin
• Reference electrode voltage is subtracted
from signal electrode
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33. Filter
• Situated Between interface and
Amplifier
• Notch filter and LPF
• Passive filters
• Notch filter remove 60Hz supply
noise
• LPF remove signals above 50Hz
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34. Amplification
• Zero trim remove dc offset
• Applied to signal amplifier
• Gain can be adjusted
• o/p will not exceed ADC I/P range
• Removing dc offset
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35. Signal Processing
• Signal is applied to ADC
channel’s of µC
• small conversion time
• Conversion : CH1,CH2…..CH8
• Data send to µC2
• µC2 store it to flash drive and
compare with look up table.
• Control the output pins 35

36. Sampling
• Done by µC
• Nyquest sampling
Fs ≥ 2Fm
Fm= 50 Hz
• Fs must be ≥ 100 Hz
• Usually 1 KHz and above is
used
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37. Requirements of Op-Amp
• High input impedance (> 10 MΩ)
• Gain > 100
• High CMMR (> 100 dB)
• Low offset voltage (mV)
• Very small bias current (nA)
• Operating voltage ±5
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38. Requirements of µC
• Speed - 25MHz
• Memory – 2kB RAM & 32kB ROM
• 8-channel ADC with 8 or 10 bit
resolution
• Serial communication ports
(SPI,USART,I2C)
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39. File format for 8 bit ADC sample
d1 d2 d3 d4 d5 d6 d7 d8 t1
d1-d8 represent the sampled byte from each channel
t1 represents the triggering and control bits
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40. With The Help of Brain Wave
Patterns We Could Trigger A
Specific Course of Action And
Hence Successfully Simulate A
Microcontroller
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CONCLUSION

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ANY DOUBTS..!!!

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