GEORGIA INSTITUTE OF TECHNOLOGYSchool of Electrical and Computer Engineering

ECE4040 Senior Design

Final Report

Fall 2001

Mind Control GroupJames Lin, Son Phan,

Michael Oduselu

I. Introduction

Purpose

Our objective is to research the use of brainwaves with computer controlled applications. We are

attempting to use the brainwaves as a medium to control a remote controlled vehicle. Our application is

just one of many of which thought controlled applications can enhance, but still proves a point of how mind

control can be used in certain applications. By using thought to control a device, a whole new unexplored

means of communication is opened up.

Goals

There are many steps in the process of developing a thought-controlled application. The

brainwaves have to be read and interpreted, processed by some software and interfaced with a device to

communicate with it. The whole process is a very large task and will take much time for just one

application. Our goal for this project is to tackle the first step of reading in the brainwaves. The

commercially available EEG machines are too costly and may not be flexible enough to meet our needs, so

we our goal is to build our own EEG monitor. Therefore, our project will mostly involve researching the

circuit and design specifications of building an EEG monitor and putting one together. Once the prototype

EEG machine is built, we hope to be able to read brainwaves and display the signal onto an oscilloscope.

II. Background Information

Potentials of Brainwave Applications

The mind holds many opportunities and great potentials for any type of applications. To harness

the mental capacities of the mind can lead to great advancements of quality of life and mental well being.

Such open possibilities are the reasons to study how we can access the brainpower and thought.

Knowledge and understanding of the brain can lead to many different applications in a variety of

different fields. The ability to use the mind alone in controlling machines opens up a whole new realm of

interaction with computers, applications, games and other devices that were never possible.

EEG Biofeedback

One such field is EEG Biofeedback. Research in this field began in the 1960s. Research in this

area was first used to attempt to control alpha rhythms in the brain. Further work in the field attempted to

control other brain rhythms to enhance and lower certain patterns. Applications such as personal self-

improvement, meditation, relaxation, brain calisthenics, and treatment or clinical disorders like ADD are all

enhanced with biofeedback.

Computer Control & Communication

Research in this area has been slow. Nonetheless thought controlled computer applications may

have breakthrough potential. Thought controlled cursors and brain-controlled games are a few of some

applications that have been developed. Research began by trying to record thoughts of movement such as

up and down. Later work has involved a different strategy where the computer adapts to other forms of

consciousness. SMR, which means sensorimotor rhythm, has the person use affective thoughts to change

their brain rhythms to control the system.

Entertainment and Virtual Reality

Entertainment applications may involve devices that use brainpower to control music and

graphics. Virtual Reality displays and light and sound machines are also applications that can be improved

with brainwave communication. Ideas into this area have included applications such as “EEG-controlled

composition and performance and audience-participations situations.” Virtual Reality ideas have also

thought of using brainwaves to control parts of the virtual realm. The system could use the brain to create a

world to match the person’s conscious and emotional state by changing colors, objects, light, and other

features of the world.

Education and Research

Currently there is little knowledge of the brain to students below the graduate level. Furthering

research into EEG and brainwave technology will give students the opportunity to learn about the mind and

provide insight on how brainwaves can be used.

Military and Commercial Applications

EEG and brainwave monitoring has been used by the military for many years since the 1960s.

NASA used brainwaves to monitor the pilot’s health and consciousness. The Air Force has also used

brainwaves to develop mind controlled cockpit controls by using the pilot’s state of attention to control a

flight simulator. In addition, the use of brainwaves for commercial applications have been used to study

people’s reaction when viewing advertisements or evaluating new products. Such information can be used

to determine the advertisement or product’s effect on the person’s interest.

Past Research

Mind control is a relatively unexplored area of scientific study. Some of the most recent

advancements in this field include a successful experiment conducted by John Chapin. Another successful

accomplishment has been made by the United States Air Force.

John Chapin and his team trained six rats to push down a lever for a reward of water. They also

installed 24 small wires into the area of the brain that controls movement in their right paw. Every time the

rat reached out to push the lever, a computer monitored its brain activity. The computer was able to

establish a pattern after monitoring the rat’s action because the neurons in the brain were signaling in a

particular way. With that brain pattern decoded, Chapin disconnected the lever and gave control to the

computer. When the computer detected the specific brain pattern of the rat reaching out to push the lever, it

delivered the water before the rat had a chance to hit the lever. Eventually, the rats ceased trying to operate

the robotic arm physically, and relied solely on brainpower.

Gloria Calhoun, an engineering research psychologist at the U.S. Air Force's Alternative Control

Technology Laboratory in Ohio, is a veteran of a special flight simulator that allows human beings to "fly"

with their brain waves alone. Unlike Chapin's work with rats, the Air Force's "fly by thought" research does

not require electrodes implanted in the brain. It monitors brain waves with sensors placed on the pilot's

skull.

In England, Kevin Warwick, a cybernetics professor at the University of Reading, had a computer

chip surgically implanted in his left arm two years ago. The chip communicated by radio waves with the

computer that runs Warwick's office building and in essence made Warwick a living node in the computer's

network. The computer monitored the professor's passage into and around the building, opening doors for

him, turning on his computer as he approached, even greeting him by name as he walked through the front

door.

"That (experience) changed my mind-set quite a bit," Warwick says. "I felt as if the computer became a

part of me, and I became a part of the computer. And I was quite comfortable with that. There was an

affinity there."

When the implant was removed after nine days, Warwick recalls feeling an odd sense of disappointment at

being disconnected from the network. He is planning to repeat the experiment next year, this time with a

more sophisticated implant capable of a wider range of communications. Among other things, he plans to

use the device to give himself a sixth sense, in addition to the five given him by nature.

With a computer chip in his arm, Warwick refers to himself as a cyborg. The term was coined in the 60's by

NASA scientists and has since become a staple of science fiction novels and movies. In a cyborg, computer

hardware has become so much a part of a human being that it creates a new species --- part machine, part

human --- with impressive new powers. The title character of the movie "RoboCop" would fit that

definition, as would the Borgs in the "Star Trek" TV series.

There may not be very much advancement in this field, but the ones that exist are exemplary. Our

goals are to make further advancements in the field using EEG and or biofeedback. Being that this is a

relatively unexplored area of study, we feel that with an ample amount of research and experimentation, the

possibilities are endless.

EEG Explained

I. EEG Functions:

The EEG is the device behind all the brainwave applications. EEG stands for

Electroencephalogram. EEG device records the bioelectric activity of the brain by means of an array of 8

or 16 surface electrodes applied to the scalp. The voltage fluctuations registered by the EEG are

summations of the constantly ongoing electrical activity of populations of neurons. Largely, the EEG

record reflects the extra cellular currents resulting from postsynaptic membrane depolarization and hyper

polarizations of cortical pyramidal neurons. Their anatomical position results in the generation of relatively

large electrical fields or dipoles.

The EEG device consists of a continuous recording of rhythmic positive/negative voltage

fluctuations in the microvolt range from 50 to 200 uV.

The EEG device records the frequencies range from 0.5 to 20 Hz:

Delta: 0.3 to 3 Hz

Theta: 4 to 7 Hz

Alpha: 8 to 13 Hz

Beta: >13 Hz

Other important patterns are spikes, spindles, K-complex

The EEG recording is state dependent and reflects ongoing brain activity. It changes as a function

of age, arousal level, sleeping stage, cerebral dysfunctions.

II. EEG Specifications:

1.Head-box: The head-box (lead plug-in box) is the means whereby the electrodes, attached to the

head, are connected to the EEG machine.

2.Switching: A description of the switching system that is used to connect the various pairs of

electrodes to the different channels (usually a minimum of sixteen) of the machine should be provided.

Switching systems are needed because, to obtain an adequate coverage of the electrical activity from

various parts of the brain for clinical diagnostic purposes, it is necessary to record from more combinations

than can typically be displayed at one time.

3.Amplifiers: The amplifiers should be completely described, including documentation that they

provide the same distortionless output over the entire EEG frequency spectrum.

4.Filters: The high and low frequency cutoff points of the low-pass and high-pass filters

respectively should be specified. These define the bandwidth of the amplifier.

5. Common-Mode Rejection Ratio: CMRR is a measure of how well common-mode voltages are

rejected by a differential amplifier incorporated in the device.

6. Input Circuit: In order to realize the CMRR quoted in the specifications of a particular amplifier,

it is essential for the input circuit of the amplifier to be balanced.

7. Noise: Connecting the inputs of the amplifier together or “short-circuiting” the input of the

amplifier and measuring the output of the amplifier can determine this noise.

8. Input Impedance: To accurately measure the voltage appearing at the scalp, the input impedance

of the amplifier should be six orders of magnitude higher than the impedance of the electrodes at the

skin/electrode interface.

9. Frequency Response: The file should describe the amplifier in terms of frequency response (Hz)

and amplitudes of the recorded signals (micro volt).

10. Impedance Checking: Typical values of impedance are less than 5 K ohms.

11.Calibration: The device should provide the capability of user calibration.

What is currently available

Engaging in brainwave research requires delicate devices which can read the potentials of the

brain. One of the main reasons that advancement in this field is slow is due to the costly resources

necessary to perform experiments. To read brainwaves, an EEG monitor is required but many are sold for

thousands of dollars. However, there are a few devices being made which may be affordable to an

individual.

BrainMaster

The BrainMaster is a general-purpose brainwave monitor that

connects to a computer. It is capable of recording 1 to 2

channels of brainwaves plus other functions such as data

storage, retrieval, real-time signal processing, display, and

feedback. Users can interface the device with their own software that they create. The cost for the monitor

is about $1000. More information about product details and specifications can be obtained at:

http://www.brainm.com

WaveRider Jr.

The WaveRider Jr. is a system capable of acquiring two

biological signals which allow the device to be used as an EEG

or EMG machine. The machine also provides audiovisual

feedback to use for relaxation and meditation applications. The

machine is more integrated and configured than the

BrainMaster, but less sophisticated. The cost for one is $950.

http://www.elixa.com/mental/wrjr.htm

POD

The POD is a personal optimization device that has 1 or 2 channels for

EEG feedback. It is advertised that it is used to study brainwaves to

improve skills such as studying or playing golf. The same company

also makes a more advance EEG system capable of handling up to 24 channels for feedback called the

NeuroSearch-24. List price is $992. http://www.lexicor.net/

ProComp+

The ProComp by Thought Technology is a highly flexible device that can measure a variety of signals.

The 8-channel system can measure EEG, EMG, EKG, skin

conductance, temperature, heart rate, blood volume pulse, and

respiration. It also uses fiber optic cable to connect to the computer

for better resolution. However, all the features come at a high cost

of $3500+ per unit. The company also sells a variety of software to interface with the ProComp.

http://www.thoughttechnology.com

Alternatives

As shown, EEG devices are highly prices devices. As more features are added, the prices of such

devices jump thousands of dollars. One alternative, which we are pursuing, is to build your own EEG

monitor. With sufficient resources and knowledge of how EEG machines work, one can build one at a

much lower cost.

III. Brainwave Mind Control Design

The objective of this section is to document and report on our current design and immediate

design goals for the brainwave project. Our main goal in this stage is to find and design a suitable

biofeedback amplifier. We have chosen to design an older version of the BrainMaster EEG monitor that

was given to us by the founder. There have been many references to the BrainMaster project and other

people building their own EEG monitor, therefore by building off something that has been tested, we hope

to encounter fewer problems.

Block Diagrams

The previous group that worked on the brainwave project came up with an overall design

represented in the block diagram below:

Figure 1. Proposed system from the previous brainwave group.

After reviewing their design and our proposed design, we have constructed a revised block diagram.

Figure 2. Revised block diagram of new system.

The new

system shows

some significant changes from the previous design. The second stage shows the amplifier and filter

combined because the EEG monitor that we are proposing to build performs both functions. The output

signal is an analog signal that will be converted into digital format through an analog/digital converter chip.

The signal is then calibrated though by adjusting the A/D circuit to get the best range of values to

be inputted into the microprocessor. This can be done with variable resistance such as a potentiometer for

each channel.

The fourth stage is the microprocessor where the signal is processed and synchronized with the

computer. This will require buying a pre-made board with a microchip and various input ports on it that is

ready for use, or building a board by ourselves. We propose buying a completed board or one with all the

parts ready to be assembled since designing a board will be a big task. John Peatman

john.peatman@ece.gatech.edu , a professor here at Georgia Tech, teaches a class about microprocessors

and may be of assistance. The board should then be connected to the computer via the serial port or USB

port depending on the type of microchip board being used. Cypress Semiconductor

http://www.cypress.com produces a full speed USB microcontroller (EZ-USB) and has many different

USB development kits, tools, and example code to work with if USB is desired. Code will have to be

written or obtained to interface the microchip with the computer at this stage.

During the code writing stage of the fourth stage, the next stage can be started. During this stage a

program will have to coded up to collect data from the microcontroller and interface it with the transmitter.

If a microchip development package with code for both ends can be obtained, these steps may be

simplified. This will most likely involve C++ programming.

The transmitter is the device that will control the car’s movement. It will have to be modified to

be controlled by the computer.

Current Design Step

The current step of designing the system will focus on building the EEG monitor that will collect,

amplify, and filter the brainwaves. We are using the plans for an old version of the brainmaster

http://www.brainm.com developed by the founder Thomas Collura. The EEG circuit is a two-stage

amplifier shown below:

Figure 3. Amplifier Schematic.

The parts for the EEG circuit are commercially available and were obtained online from Digikey

(http://www.digikey.com) and Pioneer-Standard Electronics (http://www.pios.com). The total cost for the

parts ranges about $30 - $50 depending on quality and extra parts. We used 1% metal film resistors and

5% polypropylene capacitors.

The first stage of the amplifier provides a gain of 50 from the user input. Quoted from the

brainmaster plans, “The input amplifier IC-1 is an Analog Devices AD620 instrumentation amplifier, set up

with a gain of 50. This gets the signal "out of the noise," and provides high input impedance and high

common-mode rejection. The "AC" coupling due to R4 and C4, or R5 and C5, occurs with a long time-

constant, and does not limit the low-frequency response. It also does not affect the CMRR, since it is not in

the passband. It does, however, allow the inputs to IC-1 to be biased into the middle of their common-

mode range.

The amplifier IC-2 is used to provide an integrator, used as a low-pass filter developing the

reference for IC-1. This results in a baseline-correction that produces a low-frequency cutoff at 1.6 Hz. It

also allows the output of IC-1 to operate near its center, providing good linearity.”

Stage 2 has a gain of 390 for a total gain of 19500 for the entire amplifier. In addition the second

stage provides a frequency response to 34 Hz. The schematic also shows an input voltage of 2 volts in

addition to the VSS supply voltage. This voltage is used as a midpoint between the supplies. The circuit is

specified to work with supply voltages from 5V to 36V.

A summary of the specifications are listed below:

Type: differential

Inputs: (+), (-), and "ground" return

Gain: 20,000

Bandwidth: 1.7 - 34 Hz

Input Impedance: 10 Mohms

Input Range: 200 uV full-scale

Output Range: 4 volts: from 0.0 to 4.0 volts

Resolution: 0.80 uV/quantum

Input Noise: < 1.0 uV p-p

CMRR: > 100dB

Amplifier Simulation

Before the amplifier is built on the Proto-board, Pspice implementation was conducted to

approximate the possibilities of the output waveform. AD620 and OP90 are not available in Pspice, so

ideal op-amps are used instead. In this circuit, three ideal op-amps were used to implement AD620, and

each ideal op-amp is replaced for OP90. The following is the implementation of amplifier in Pspice:

Figure 4. Circuit used for SPICE simulation.

The next step is simulating and examining the output waveform. If the input is 200 uV, then the

output is expected to be 4 V. That means the total gain is 20 thousand. The following is the output and

total gain waveform copied and pasted using Kaladagraph software. The values are not exact as expected

because there are some differences between ideal op-amp and AD620 and OP90. However, the

approximations convince the high possibilities of a working amplifier.

Figure 5. Simulation of output voltage vs frequency.

Once simulation confirmed that the amplifier should work, it was built on a breadboard for testing. A

picture of circuit is pictured below:

Figure 7. EEG circuit on proto-board for testing.

Figure 6. Simulation of gain vs frequency showing cutoff at about 30 Hz.

The next steps in the design plan are to add on another stage for the midpoint voltage. The circuit

should be able to operate with just the two stages and two power supplies, but one power supply is more

convenient. The brainmaster plans also provide the schematics to implement the midpoint voltage and a

circuit on obtaining a clean voltage source.

Figure 8. Midpoint voltage circuit.

Component values for Figure 4 are:

R0: 50K 1% metal film

R1: 100K 1% metal film

R2: 100K 1% metal film

C3: 0.1 uF mylar

Figure 9. Circuit to get a clean power supply.

Figure 5 shows a circuit used to obtain a clean regulated power supply. The components included a 7805

regulator. The other components values are:

B1: 9V battery, or 6 "AA", "C", or "D" cells

C1: 10uF tantalum

C2: 0.1uF mylar

Once the circuit has been assembled, we hope to see some oscillations on the oscilloscope. The

circuit can be tested by trying to record the heartbeat from one hand to the other. If a good signal is

obtained, the circuit will be used to try to obtain a brainwave. To improve results, the circuit can be

soldered onto a pre-holed prototype board to reduce capacitive coupling.

Using the Brainwaves

First of all, we need to know if we have or can attain a common thinking pattern. Our patterns

differ in function of geographic origin, tradition and culture and

access to current technological developments. Unfortunately, the function of the native language is

different as well, but we can learn and adapt. Modern technology is learnable, so it may be possible to

learn a common representation of ideas (after all, we are the same species, so we should have similar

thinking patterns and learning capabilities).

The question is: how do we get to these common thinking patterns? Again, if we find a way to

communicate with a computer, it is doable. The computer will learn from different groups of people and

then look for similarities. Machine learning (and reinforcement learning) can be used to discover the

specifics of thinking of a group of people or individuals.

The brain operates in different states which output different types of waves. One possible of using

the waves to navigate by controlling which state our brain is operating in with emotions. If a person can

train the way he feels, then he may have the power to control the output the EEG to match his thoughts and

emotions. Figure 6 shows a guide of which type of brainwaves are associated with different emotions.

Figure 10. Guide to brainwave navigation.

IV. Results

The current progress of the EEG circuit has progressed to the soldering of the complete EEG

circuit. In the initial tests, the circuit was not performing as expected. There was no output coming from

the amplifier. After an analysis of the circuit, the input was discovered to not be passing into the op-amp

circuit. The problem was in the circuit schematic which told to ground the resistors at the input. The

resistors were instead connected to AGND and the circuit was seen to be amplifying the inputs. When the

three terminals of the EEG were connected together, the output was flat signaling that something was at

least working.

The EEG circuit was then tested by attempting to display an EKG signal by using the heart. The

electrodes were held across the chest to amplify the voltage across the heart. Using the oscilloscope in the

lab, the signal was scaled down until only a vertical line was seen moving across the screen. The line was

seen to jump at synchronous moments with the heartbeat. However, this only showed that the amplifier

was working but a visible waveform was not visible. An oscilloscope in the 3042 lab was used next to see

if a wave form could be obtained.

Figure 12. Heart beats as seen on the oscilloscope.

Figure 13. Heart beat observed more closely.

Figure 11. Flat signal when all input grounded.

The figures show that the waveform is pulsed when a heartbeat occurs. However, the waveform does not

match a conventional EKG signal shown in Figure 14. This is due to interference from the breadboard and

should be eliminated when the circuit is soldered together.

After the amplifier was shown to work the circuit was soldered together on a protoboard to attempt to

increase its performance. New parts were ordered as well as electrodes to try to obtain better and cleaner

signals.

Figure 14. Normal EKG signal.

Figure 15. Electrodes and wires for better signals

Figure 16. EEG soldered on protoboard.

Figure 17 below shows one of the screens of the BrainMaster software. The waves are seen to be analyzed

to see what frequency is being inputted into the EEG module.

The following figures show some of the results obtained when the EEG was connected to the forehead:

Figure 18. Forehead readings.

Figure 17. Screenshot of brainmaster software.

According to Figure 7, the signal is seen to have frequencies in the range of the specified bandwidth of

30Hz. The frequencies in the figure are shown to be in the Alpha and Beta ranges, however, the

frequencies were fluctuating a lot which may be due to the lack of control of brainwaves.

According to above Figures, the signal is seen to have frequencies in the range of the specified

bandwidth of 30Hz. The frequencies in the figure are shown to be in the Alpha and Beta ranges, however,

the frequencies were fluctuating a lot, which may be due to the lack of control of brainwaves. The waves

shown in the screenshot are similar looking to the ones captured by the oscilloscope. The waves shown in

the screenshot are similar looking to the ones captured by the oscilloscope. In order to get a good value for

the frequency of the signal, some filtering must be done. Digital filtering by a computer is recommended.

Just to show that the signals obtained were actually coming from the head, the jaw was flexed by biting to

interrupt the signal.

Exteneded Results

Once initial measurements showed that the EEG circuit was functional, more tests were taken to

see what could be measured and to investigate the possibilities of using such measurements for future

applications.

Using the HP oscilloscopes in the 3042 labs, the signal was tested to see if a FFT response could show a

dominant frequency of the brain.

Figure 21 shows that the FFT response when the circuit was connected to the head. The

oscilloscope was scanning frequencies up to about 125 Hz. The left side of the figure corresponds to 0 Hz

which shows that the frequency range was as expected from about 0 – 30 Hz. Figure 22 and 23 below show

a closer look at the response showing on the frequencies up to about 25 Hz.

Figure 19. One jaw flex. Figure 20. Three jaw flexes.

Figure 22. FFT of brainwave using oscilloscope.

Figure 21. FFT (lower signal) reponse of brainwave signal.

Figures 22 and 23 show closer look at the FFT of the brainwave signal. As the measurements

show, the FFT was a consistent signal with some pattern. The spike near the center suggests the location of

the dominant frequency of the brainwave. The range of frequencies was about 0 – 25 Hz which puts the

spike at about 10 Hz. This frequency corresponds to Alpha waves. Alpha waves are noted to be best seen

in the frontal region of the head and can be brought out by relaxing. The measurements were taken on the

forehead while relaxing so the results match what was expected. Testing of the FFT was more difficult

using the oscilloscope since as the range of frequencies was reduced, and the oscilloscope took a much

longer time to update the response. Therefore, it was hard to see if any changes occurred due to thinking

Figure 23. Another FFT response showing consistency.

differently because the refresh time was very slow. Using a computer to do the filtering may yield better

results for testing.

To further explore the application of using muscles in the head to create signals, such as was

shown by the jaw biting results, measurements were taken for eyebrow movements.

Figure 24. Signal produced by raising eyebrows once.

Figure 25. Signal produced by raising eyebrows twice.

As shown if Figures 24 and 25, there was a very distinct signal produced as a response eyebrow movement

and was fairy easy to create. Additionally, Figure 26 shows a signal as a result of a hard blink.

Conclusions

As the design process as shown, we are one step closer to using brainwaves to control for the

remote controlled car or any other application. The EEG amplifier circuit provided by Thomas Collura

worked nicely in obtaining brainwaves. With first step complete, the next task would be to convert the

signal to a digital one to send to a computer. Then the computer could do some filtering and processing to

get control an application. However, as a side effect from trying to measure brainwaves, it was discovered

that signals from muscle movement in the head was far easier to read and create than brainwave signals. In

addition, one must consider that amount of training required to be able to alter brainwaves at will. With

muscle movement, the signals can be manipulated as naturally. However, there is a disadvantage of only

being able to have high and low signals. With brainwaves, a range of frequencies can be used. There is a

possibility that by using different muscles on the head, different unique signals can be produced.

Nevertheless, after building the EEG, for an application such as controlling a car, using muscle movements

in the head would probably be the best solution as moving a vehicle requires instantaneous reactions.

Testing and implementation would also be simplified.

Figure 26. Signal response to a hard blink.

Group Participation

Also important to note is the group participation for this design project as much of the work was

not distributed evenly. James did much of the work dealing with finding out of what was available and

obtaining the design for the brain master EEG. In addition, much of the process of getting the parts,

assembly of test and final circuits, and testing was done. Compiling reports, presentations, and website was

also a task James performed. Son did much of the background work on EEGs and the amplifier, and also

worked on getting the design to simulate correctly. Michael was not involved much in the design process.

His work was mainly in doing some background research.