TRIBHUVAN UNIVERSITY
INSTITUTE OF ENGINEERING
Kathmandu Engineering College
Department of Electronics and Communication Engineering
Proposal on
Brain Control System
Submitted By
Ramu Raut
Rohit Singh
Sherya Dhayal
Ram Prasad Gajurel
Submitted to
Locus 2014
Date: 2014-06-11
Table of Contents
Abstract ......................................................................................................... i
1.
Introduction ............................................................................................................ 1
1.1.
Background .................................................................................................. 1
1.2.
Objective ...................................................................................................... 3
1.3.
Scope and Application ................................................................................. 3
2.
Literature Review ................................................................................................... 4
3.
Methodology........................................................................................................... 5
4.
Block diagram......................................................................................................... 6
5.
References .............................................................................................................. 7
Abstract
A brain is the most important part of all living beings. It plays an important role in decision
making, sensing and overall control of all the body activities. Technically, we can say the brain
as a multifunction microcontroller performing multi task at the same time. From analytic point of
view we human beings are the self-motivated, self-controlled robot having decision making
power where this self-controlled capacity comes from the neural network inside the brain which
produces different level signals on the basis of different activities of the body. The human brain
is made up of billions of interconnected neurons about the size of a pinhead. As neurons interact,
patterns manifest as singular thoughts such as a math calculation, and broad emotional states
such as attention. The average human thinks 70,000 thoughts each day. As a by-product, every
interaction between neurons creates a miniscule electrical discharge, measurable by EEG
(electroencephalogram) machines. By themselves, these charges are difficult to measure from
outside the skull. However, a dominant mental state, driven by collective neuron activity created
by hundreds of thousands concurrent discharges, can be measured.
Controlling anything by electric pulses coming from Brain is known as brain control. This
proposal includes the detail mechanism, methodology, scientific hypothesis from spiritual
science related to brain control to the modern technology used for studying the brain activity.
Imagine transmitting signals directly to someone's brain that would allow them to see or feel
specific sensory inputs, controlling computer, mobiles, or any machinery by our thought. This
paper explain smoothly how our brain works and what kind of signals it transmit and how can
we preserve that signal for further processing.
1. Introduction
1.1. Background
Through a century of experimentation, neuroscience experts have determined where specific
activity occurs within the brain. For example-Motor control of limbs occurs in the top of the
brain, Vision is processed in the back of the brain. From an evolutionary point of view, these
basic functions are present in most animals. As humans evolved into more intelligent creatures,
the pre-frontal cortex in the front of the brain is where higher thinking occurs. Emotions, mental
states, concentration, etc. are all dominant in this area.
Different brain states are the result of different patterns of neural interaction. These patterns lead
to waves characterized by different amplitudes and frequencies.
Our brain creates brainwave activity every moment of our life, day and night, awake or asleep.
Each state is accompanied by specific brain wave patterns. On the basis of different signal
produced by mammalian brain researchers have defined the different band of signal on the basis
of different activities performed. The different band of signals are
A. Alpha band
B. Beta band
C. Theta band
D. Delta band
E. Gamma band
Alpha band are 8 to 13.9 Hz. Waves in this band specially signify relaxation and closing of the
eye. They are measurable at the back of head.
Beta band are the second most prominent and can be anywhere from 13 to 30 Hz. They signify
mental activity and mental concentration.
Theta band lies from 4 to 7.9 Hz and are mostly observed when the mind is in idle state.
Delta band waves are form 1 to 3.9 Hz and are prominent during sleep.
Gamma band waves are above 30 Hz above and are associated with some cognitive and motor
function.
For example, the abnormal activity in Alpha band can be used to detect ‘Coma’.
This process of reading, recording and analyzing these different wave patterns of signal is known
as Electroencephalography (EEG) and the machine used is EEG machine. The
Electroencephalography technique involves the detecting electrical signals created by neurons
firing in the brain. Specific thoughts pattern can be identified by recording and correlating the
resulting electrical signals. Electroencephalography (EEG) signals in human were detected in
1924 by Hans Berger. Later, this method was used by medical experts for diagnosing the
different disease like epilepsy and specific mental disorder.
1.2. Objective
The objectives of this project “Brain Control System” is as follows:


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
Approach to create new way of communication between Human and Machine.
To make the technology more human friendly and interactive.
To control bionic system using brain by physically handicapped people.
To give concept of Psychic weapons.
1.3. Scope and Application





It can be used to drive “Bionic” Limbs.
It can make the technology more interactive and user friendly.
It can be used to drive a Wheelchair and a Car.
It can be used in police department to identify lie of criminal (lie detector).
It can be used for interfacing with electronic system (Brain computer interface).
2. Literature Review
Brain Control System technology has been studied with the fundamental goal of helping disabled
people communicate with the outside world using brain signals. Brain System Interface creates a
new non-muscular channel for relaying a person’s intentions to external devices such as
computers, speech synthesizers, assistive appliances, and neural prostheses. It is particularly
attractive for individuals with severe motor disabilities. Such an interface would improve their
quality of life and would, at the same time, reduce the cost of intensive care.
A Brain Interface System is a system that can recognize a certain set of patterns in brain signals
following five consecutive stages: signal acquisition, preprocessing, feature extraction,
classification, and the control interface. The signal acquisition stage captures the brain signals
and may also perform noise reduction and artifact processing. The preprocessing stage prepares
the signals in a suitable form for further processing. The feature extraction stage identifies
discriminative information in the brain signals that have been recorded. Once measured, the
signal is mapped onto a vector containing effective and discriminate features from the observed
signals. The extraction of this interesting information is a very challenging task. Brain signals are
mixed with other signals coming from a finite set of brain activities that overlap in both time and
space. Moreover, the signal is not usually stationary and may also be distorted by artifacts such
as electromyography (EMG) or electrooculography (EOG). The feature vector must also be of a
low dimension, in order to reduce feature extraction stage complexity, but without relevant
information loss. The classification stage classifies the signals taking the feature vectors into
account. The choice of good discriminative features is therefore essential to achieve effective
pattern recognition, in order to decipher the user’s intentions. Finally the control interface stage
translates the classified signals into meaningful commands for any connected device, such as a
wheelchair or a computer. The Brain System Interface design was considered too complex,
because of the limited resolution and reliability of information that was detectable in the brain
and its high variability. Furthermore, Brain System Interface systems require real-time signal
processing.
3. Methodology
The method for studying and recognizing the brain wave or signal of different spectrum (band)
of brain like alpha band, beta band, gamma band, theta band, delta band etc. are quite difficult
because these spectrums of signals are very less in magnitude, frequency and are also
indeterminestic. The special study should be done to deal with this signal i.e. special type of
system with high precision and accuracy should be made to capture these brain signals. To
capture these signals medically a sensor or transducer named electrode is made which could
capture this signals with high accuracy. The best used electrode is defined to be Ag/AgCl
electrode. There are two types of electrodes i.e. Active electrode and Passive electrode.
Difference between active and passive is in active electrode there is pre-amplifier and in many
case Radio-Frequency suppresser in same electrode so we doesn’t need to design amplifier but in
passive electrode there is pre-amplifier. So in this project we are using active electrode. We will
use the same type of electrode putting into the o forehead of the brain with some type of
conducting gel(saline) for decreasing the skin impedance. A electrostatic discharge protection
circuit is also needed to protect the human beings from the electric shock.
When the signal is received by the transducer its goes to the amplification and filtering stage.
The amplification is done by using the medical instrumentation amplifier having high
CMRR(Common Mode Rejection Ratio) ,low offset voltage. The stage to stage filtering and
amplification should be done to free the signals from noise. The amplification should be done up
to the operating voltage. Since, our system is also easily affected by the main power signal
frequency (i.e. 50 HZ) and other surrounding system signal like cell phone frequency. This type
of signal is also noise for our system and should be filtered. After the amplification and operating
stage is completed the signal is converted digitally and signal is further processed.
In our project the sensor attached to user head has filtering stage and amplification stage. The
analogue data of sensor is digitalized by Thinkgear technology developed by Neurosky company
and combined with NeuroSky's proprietary eSense™ algorithms. The digitized raw data came
from electrode headset is passed to receiver by radio frequency technology. The raw data is
analyzed by interfacing it with MATLAB computer software. The comparison between raw
digital value and graphical value of user brain waves we are able to distinguish the attention and
meditation state of human brain. And we also able to find the body artifacts such as eyeblink,
head-movement, muscle movement etc. After finding the value of three parameter attention,
meditation and body artifacts we implements it in arm processor based operating system
platform Raspberry-pi and able to used its General purpose input output to run robots and
wheelchair titled as ‘Brain Controlled Vehicle’. Another important application we have created
is Blink-Talker. It is a desktop application created by Python programming language in which
user can select the word without involvement of physical organs. User can select the word by eye
blink and computer speaks what words are selected. This system is more like Stefan Hawking
computer for Hawking speaking purpose. So people who can’t talk can talk using Blink-Talker.
4. Block diagram
Electrode
Amplification Stage and
filtering stage
Active low pass filter (below
50Hz) with amplification
Input stage
Electrostatic discharge and
user protection circuit
First filter stage(Active high
pass filter i.e. above 0.16Hz)
Analog to digital
conversion
Instrumentation
Amplifier
Signal processing
unit
System or device
Fig 1.1: Block diagram for producing and identification of EEG signal
5. References
www.biomediresearches.com
www.neuroscience.com
www.support.neurosky.com