Download Spring 2002

EEG Biofeedback
Final Report
Adrian Smith, gte198f
Daniel Shinn, gte539f
Ken Grove, gte262f
ECE 4006 - Group N1
Spring 2002
April 23, 2002
Georgia Institute of Technology
College of Engineering
School of Electrical and Computer Engineering
Abstract
The objective of this project is to continue the work of previous groups and design
a system that will ultimately be able to control a remote vehicle using brainwaves. From
the digital aspect we are not necessarily concerned if the control signal is a brainwave,
rather the focus is on the identification, process, and assignment of these signals to
specific commands. In order to achieve these plans, muscle movement signals will be
used. These signals will be obtained via an analog-to-digital data acquisition card, and
manipulated through computer software. This report will help future groups to carry out
the next steps in realizing the goal.
EEG Background
The study of the brain has intrigued scientists and researchers for hundreds of
years. However, recently the advancement in technology has opened a broad world of
study associated with the impulses produced by the brain. As the study of these impulses
continues into the future, humans will eventually have the opportunity to control their
surroundings with their mind. Currently, there are devices that record these impulses and
communicate back to the user through a computer interface to combat and help the user
overcome many neurological disorders.
In the 1920’s a German psychiatrist named Hans Berger was the first to record the
electrical activity of the brain. The recorded impulses are known as the
electroencephalogram (EEG). The impulses come from the change in electrical currents
across neuron membranes. Neurons transport these currents as information that can be
recorded as electrical or magnetic fields through electrodes which are attached to the
scalp. These potentials are recorded usually at sixteen points on the scalp through an
array of sixteen electrodes.
The detectors in the electrodes, which were originally developed in the 1970’s,
are called “superconducting quantum-interface devices”, or SQUIDs for short. The
resistance of the internal conductor which is low at extremely low temperatures picks up
the small currents associated with brain information transfer. Voltage potentials of 50 to
200 µV can be detected. The drawback to this is the detector picks up all the information
in that region. Thus, differentiating between involuntary actions, such as breathing, and
voluntary actions, such as limb movement, is the main hurdle for the advancement of
EEG devices. Also, sweat and head movement can lead to misinterpreted readings.
It has been found that these currents fluctuate with time in a rhythmic pattern.
Thus, after a frequency analysis, or Fourier transform, on an EEG sample, a band of
frequencies can be found to dominate the brain’s “rhythms” as seen in Figure 1.
Figure 1. Fourier Analysis of a Sampled EEG recording.
Figure 1 illustrates the approximate range of the frequencies involved in the
brains normal rhythms. In fact, the range of 0.3 to as high as 35 “rhythms” per second
can be associated with the human EEG. Ranges below 4 Hz are found when the body is
in the delta stage in which the body is in a deep dreamless sleep. The range from 4 to 8
Hz occurs during the theta stage which is associated with the consciousness that occurs
on the brink of sleep in which dreamlike mental images can surface. The alpha stage
produces rhythms between 8 and 13 Hz. This rage occurs when the body is relaxed,
drowsy, or mentally unfocused. When the eyes are open, and the body is reacting to the
environment, the beta stage sets in. Beta waves are above 13 Hz, and during extreme
excitement or anger, can reach in the mid-30 Hz range. Most of the functional rhythms
recorded by current technologies concentrate on the theta and beta range of frequencies.
Every person responds differently rhythmically to a stimulus; therefore, what can
be concluded in an EEG with one person’s sample will not be the same as the next
person’s EEG. Thus, the role of customization in an EEG technology is essential for its
optimal operation. However, the approximate stage can be linked to a particular
stimulus.
Figure 2. Production of Alpha Waves After Absence Visual Stimuli.
As Figure 2 illustrates, beta rhythms can be found when the subject’s eyes are open.
Large alpha rhythms around 11 Hz are found when the eyes are shut, free from any visual
stimuli. The large spike in the first and second electrode corresponds to the muscle
movement of the eyelid and forehead.
The change in voltages can be linked to a stimulus. The term event-related
potentials (ERPs) are given to the flux in the wave pattern when a stimulus such as an
audible tone is sounded. If the subject is told to concentrate on the presence of a tone and
press a button when he/she hears the tone, a sequence of wave patterns can be found. If
these samples are averaged together, Figure 3 results.
Figure 3. A One Second Sample of an ERP is Averaged.
The final output waveform was found by amplifying the peaks and attenuating the noise
digitally. Thus, an estimate of the noise and the average waveform for the stimulus was
found. A slight negative drop is associated with the following of the main peak. This
negative wave called the N2 wave and is caused by the conscience recognition that the
stimulus exists. The positive peak occurs when the brain identifies what the stimulus is
and communicates it to the rest of the brain and body.
Today, EEG is being used to help aid in the diagnosing of neurological disorders.
Attention Deficit Disorder (ADD), alcoholism, and paraplegics, just to name a few. By
identifying the abnormal brain activity, usually in the alpha stage, EEG biofeedback can
be used to greatly aid in increasing a specific range’s activity. For example, a group of
alcoholics were studied on an EEG biofeedback machine. During rehabilitation, they
were given specific stimuli to compensate for the lack of alpha rhythms that they
normally would obtain by drinking. After the rehab session commenced, the abstinence
rate was eighty percent compared to the normal twenty percent found in pervious groups.
Another success of using the EEG biofeedback method was found in children
with ADD. Children and adults in this state lack beta waves associated with alert
concentration. People with ADD have an over abundance of theta waves which
constitutes for the constant day dreaming found with the disorder. Again, the
biofeedback device was used to find stimuli and pharmaceuticals that would best match
the person. Dramatic scholastic advancement up to two and a half years in grade level
achievement, and an IQ increase of as much as 15 points was achieved. From
neurological disorders, to head injuries, to epilepsy and beyond, EEG biofeedback has a
promising future for the well being of our human race. As more research is being done
with this technology, the possibilities are endless as the mind’s imagination.
Related Research Studies
For many years it was believed that electroencephalographic (EEG) activity might
provide a nonmuscular channel for sending messages and commands to the external
world via a brain computer interface (BCI). A BCI is a system that derives meaningful
information directly from the user’s brain activity in real time.
Within the past decade, several productive BCI research programs are developing
systems to assist disabled individuals. Facilitated and encouraged by new understanding
of brain function, by the advent of powerful low-cost computer equipment, and by
growing recognition of the needs and potentials of people with disabilities, these
programs concentrate on developing new augmentative communication and control
technology for those with severe neuromuscular disorders, such as amyotrophic lateral
sclerosis, brainstem stroke and spinal cord injury.
Currently BCIs are an embryonic technology that to not possess the speed,
accuracy, or ease of use to replace conventional interfaces such as a keyboard or mouse.
The maximum information transfer rates have a limited capacity of 25 bits/min. The
immediate goal is to provide users who may be completely paralyzed a means of
communication and possibly control of a neuroprosthesis. Research is focused on
improving these systems to be of value to many different people in a wide variety of
situations.
The Wadsworth Center in Albany is one of the world’s leading BCI research labs.
The main focus of their research is using the mu (8-12 Hz) and the beta (13-28 Hz)
rhythms in the EEG for communication. Subjects learned to move a cursor on a
computer screen with biofeedback. A fast Fourier transformation (FFT) algorithm was
used to calculate the power of the mu rhythm every 200 ms of EEG derivations on the
left and right sensorimotor cortex. These power values were converted into horizontal or
vertical cursor movements by linear equations. The coefficients of the linear equations
were updated after each trial. The classification accuracy was around 70 % to 80 %.
Additional BCI projects are being developed at the Graz University of
Technology in Austria. Their system uses oscillatory EEG signals as input and provides
a control option by its output, such as cursor control, selection of letters or words, or
control of a prosthesis or orthosis. In early trials, EEG patterns during willful limb
movement were used. During these experiments, a cursor’s movement was controlled
depending on planning of hand or foot movement. Extensive off-line analyses have
shown that classification accuracy improved when the input features, such as electrode
positions and frequency bands, were optimized in each subject. In addition to studies on
healthy individuals, BCI experiments were also performed with patients with an
amputated upper limb, spinal cord injury, and Amyotrophic Lateral Sclerosis (ALS).
The Neil Squire Foundation in Canada is a non-profit organization whose purpose
is to create opportunities for independence for individuals who have significant physical
disabilities. The basis of their research is brain signals related to voluntary movement.
These movements, called motor potentials, are produced by the motor cortex prior to and
during voluntary movements of the body. Motor potentials are produced as a result of a
self-initiated cognitive process. Work is being performed to utilize these signals to
control a given peripheral device.
One of the greatest challenges researchers face in the utilization of brain signals
related to cognitive processes is the establishment of a signal processing method that can
extract event related information from a single record of EEG. Processing schemes have
been proposed but are highly dependent on fundamental assumptions about EEG
characteristics, which at the present time are not well understood. The heavy reliance on
the EEG also tends to restrict BCI research. Experimenters favor brain responses located
close to the scalp, such as P300 and VEP, which are easy to detect and have the strongest
signals. This limits experimenters’ ability to observe other brain responses than those
that they are specifically looking for.
Much work needs to be carried out and many significant problems must be
overcome before a practical BCI is possible. The nature of motor potentials in patients
with disabilities must be better understood and methods to deal with artifact
contamination of the EEG signal must be developed.
Successful operation requires that the user have control over the signal features
and that the BCI correctly derive the user's intentions from them. The user and the BCI
system need to adapt to each other both initially and continually so as to ensure stable
performance.
Project Focus
Developing a BCI for use in controlling a paraplegic’s wheelchair is a large
project with many aspects. A raw EEG must be amplified, filtered, digitized and
processed before it can be interpreted for use in controlling a wheelchair. Using the
amplifier designed and tested last semester, we will continue the project by filtering and
digitizing of the EEG signal. Once the signal is in a format that can be processed we will
test various algorithms that have been proven efficient to discern different EEG signals.
The ultimate goal is to distinguish between various signals so that a human can learn to
control a remote control vehicle using only their brainwaves. This remote controlled
vehicle will simulate a mobile platform that is similar to that of a wheelchair.
Design Goals
The digital EEG design groups have three primary goals for the overall project.
The first goal is to test and understand the design produced by a previous group. This
will require our group to be familiar with how they designed and built the amplifier.
During this process we will compile a bill of materials for ordering parts to replicate the
design. This will need to be completed early in the design process so that we can have a
working test bed to base our continued work upon. The second primary goal is to
become familiar with the operation of the analog-to-digital converter that has been used
in the past to control a mobile vehicle. Part of this goal will be to relocate the board to a
computer that will be available for use after this semester. The group will also look into
the feasibility of replacing the existing card with newer PCI card technology. The final
goal of the project is to interface the amplifier with the A/D card and process the data
collected. During this portion of the project we will begin to interrupt input signals as
different commands and time permitting we will output control commands to a mobile
vehicle.
The Amplifier Board
The amplifier that was built in a previous semester was designed following the
specifications furnished by Thomas Collura, the founder of Brainmaster. Brainmaster is
a commercially available product that measures brainwaves through a PC. His design
called for a 2-stage amplifier, which was supplied with power by a 7805 voltage regulator
circuit and a midpoint voltage circuit.
Stage 1 of the amplifier, shown on the left side of Figure 4, consists of a high
impedance amplifier with a gain of 50. It also has a high Common Mode Rejection
Ratio. This stage provides noise reduction and signal centering for the higher
amplification of the second stage. The OP-90 in stage 1 is used for baseline-correction.
Figure 4. Amplifier Design derived from Brainmaster.
Stage 2 of the design uses an OP-90 to amplify the signal by 390 times. This
allows a very weak EEG signal to be digitized and analyzed on a computer. A voltage
ranging from 5 volts to 36 volts powers the amplifier. This is accomplished through the
addition of a power supply circuit for a clean and level voltage supply. The 7805 chip
accepts the varying voltage between 5 and 36 volts and dissipates the excess voltage to
always output a clean 5 volts. Capacitors are also used in the power supply design to
make the output more stable. This allows the circuit to be powered through the use of a
9-volt battery. The power supply also contains another OP-90 in combination with
resistors to obtain 4 and 2-volt supplies for use in the amplifier. Figure 5 displays the
overall specifications for the amplifier.
Type:
differential
Inputs:
(+), (-), and body ground
Gain:
19,500
Bandwidth:
1.7 - 34 Hz
Input Impedance: 10 Mohms
Signal Input Range: 200 uV full-scale
Signal 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
Power Requirements: 5 to 36 Volts
Figure 5. Amplifier Specifications
Purchasing Parts
Figure 6 on the next page shows the parts list for constructing an amplifier similar
in design to that produced by the previous group during fall semester 2001. Five percent
resistors were acquired from the labs on the third floor of the Van Leer building. The
remaining components were ordered from four separate vendors. Digikey
(www.digikey.com) was used to supply the capacitors. The voltage regulator and two
different IC’s were ordered through Pioneer Standard (www.pois.com). At this point we
ran into a supply problem. The OP-90 in the 8-pin DIP package was not in stock when
the order was to be placed.
Resistors:
(1) 10K 1/4W 5%
(2) 1K 1/4W 5%
(3) 130K 1/4W 5%
(2) 200K 1/4W 5%
(2) 10M 1/4W 5%
(2) 200K 1/4W 5%
(1) 51K 1/4W 5%
Capacitors:
(1) 0.47uF polypropylene (P474J) [$1.62]
(3) 0.1uF polypropylene (P104J) [$0.74]
(2) 0.001uF polypropylene (P103J) [$0.45]
(1) 10uF 6.3VDC Tantalum [$0.52]
Integrated Circuits:
(3) OP-90 amplifiers [$2.35]
(DIP package was not available when placing orders so SOIC package was substituted with the
use of an 8-pin SOIC to DIP adapter. Price reflects cost of DIP package, as this should be
ordered in future semesters.)
(1) 620AN amplifier [$5.92]
(1) LM7805C voltage regulator [$0.43]
Other:
(1) Set of 3 conductor signal leads [$14.40]
(3) Disposable electrodes for each testing session [$0.24]
(1) Pre-holed circuitFigure
board [$8.49]
6. Bill of Materials (per unit cost in brackets).
When research was done to determine when the part would be back in stock, it was
discovered that all major distributors searched were out-of-stock and were quoting the
same date of March 21, 2002 as the next expected in stock date. A call to one of these
distributors revealed that a production problem had depleted on hand stocks and the
distributors were waiting on a shipment from the manufacture. A solution had to be
devised. Since the SOIC package of the OP-90 was in stock at the same price per unit.
The decision was made to purchase the SOIC package version of the OP-90 and to also
purchase an 8-pin SOIC to DIP adapter distributed by Digikey. This allowed for the
work to begin on construction of the boards two weeks earlier than if we had waited until
the DIP package became available again. There was a delay in ordering the adapters as
Spring Break and the unfortunate timing of the purchaser’s office and phone number
change made communication difficult over break.
Before the discovery of prototype boards by our instructors, an order was placed
with Marlin P. Jones & Associates (www.mpja.com) for the same type prototype boards
that were used in past semesters. The final supplier was MVAP Medical Supplies, Inc
(www.mvapmed.com). Their selection of electrodes and lead wires was hard to beat.
We ordered leads in packs of fives and separated them into groups of three wires for the
separate groups. Two different size electrodes were ordered in bags of 50. These were
then separated into smaller numbers for each group to use.
The unit cost per item is also listed in Figure 6. These unit costs reflect quantity
discounts encountered in ordering supplies for all four neural impulse groups. With the
quantity discounts the total cost per board is $42.27. This could be substantially reduced
if cheaper testing leads and cheaper prototype boards could be sourced. These two items
make up 52% of the total board cost.
Given the EEG amplifier design, we can proceed with the design of capturing the
signal and manipulating it to our needs. The proposed design for this project is illustrated
in Figure 7. This design captures the signal from the EEG amplifier and manipulates it
using a command interface to control a mechanical device.
Figure 7. Proposed design for EEG signal manipulation.
The Analog-Digital Converter
The existing board used by last semester’s group was a Keithley DAS-1701ST.
However the card and the computer it is installed in are both borrowed and may not be
available for further use. The solution to this dilemma was to order a new card for our
continued use. The data acquisition board optimal for this application is the Keithley
KPCI-3107 board. The board contains all the necessary elements needed for proper data
capture.
The Keithley KPCI-3107 board has a PCI interface for fast data transfer to the
microprocessor of the computer. It has a sampling rate of 100kSamples/second. This
will enable us to not only obtain a clear sample of the desired low frequency signal, but
also manipulate the high frequencies that were not filtered out in the transfer of the signal
from the EEG amplifier to the interface which connects via cable to the PCI card. This
can be seen in Figure 8 below.
Figure 8. Photo of Keithley KPCI-3107 Data Acquisition Board.
The key features for this board are listed below:
Maximum sample rate of up to 100kS/s
16-bit inputs: 16 single-ended or 8 differential inputs
32 digital I/O lines
24 software programmable ranges
12 gain ranges (1, 2, 4, 8, 10, 20, 40, 80, 100, 200, 400, 800)
Extensive triggering options
DriverLINX™ and TestPoint™ software drivers
LabVIEW™ VIs
VisualSCOPE™ digital storage oscilloscope
ExceLINX™ (Excel add-in)
There are many advantages to this data acquisition board compared to competitors
other than its price ($680 with educational discount). The primary reason for choosing
this board is to stay with the Keithley brand and retain the ability to reuse the software
code written by the previous group. It also features an AutoZero capability that filters out
drifts in the signal acquisition. In addition the board allows assignment of a gain specific
to each channel, which can improve the flux in gain associated with the electrode or the
EEG amplifier.
After researching more about the KPCI-3107 data acquisition board, it was found
that this device was the most optimal for our application and for future research on the
EEG project. The board was ordered, but the cables and connectors to interface with the
EEG amp were not included. After talking to an engineer at Keithley Instruments, two
CAB-1284CC-2 cables were ordered along with two SAP-36 breakout boards. The twometer cables attach to the KPCI-3108 board as seen in Figure 9 below.
Figure 9. Connection of the KPCI-3108 board to the appropriate accessories.
There is an analog input connection and a digital output connection as seen above.
From the STP-36 breakout boards, the external connections of the EEG amp and the
future digital IR processing can be done. The analog connection allows 16 single ended
inputs or eight differential inputs. If one of the single ended inputs is used, the signal is
measured with respect to a reference ground (pin 17) of the breakout board. Figure 10 is
a photo of the analog breakout board connected to the PC.
Figure 10. Breakout Board Connected to the PC.
Because of the vulnerability of the open-air STP-36 to shock and noise, a new
cabinet must be built with the appropriate leads to acquire the signal from the EEG amp.
The current cabinet features a metal case with the breakout board mounted inside. Cold
connections are available but instead they are wired to female banana plug connectors.
This allows any type of input to be used and changed quickly and easily. For our purpose
alligator clips are used to connect to the electrode pads. This simple and effective design
can be duplicated for the new box. The only difference is that there will be two breakout
boards, one for analog I/O and the other for digital I/O. Because of time restraints it will
be unlikely that we will be able to build such a box. This may be left for future groups to
design and develop.
Installation and Testing of the Keithley KPCI-3107 Board
The computer in which our group installed the data acquisition board was a
Microsoft Windows 2000 Professional platform. Because this platform is Windows NT
based, it was our assumption that the drivers included on the CD that came with the board
were sufficient enough for the Windows 2000 platform; this was not the case. After
installing the drivers and interfaces from the CD given, the KPCI-3107 board was
manually installed in the computer. During boot-up of the computer, the system froze on
the Windows 2000 splash screen. We then uninstalled the drivers and removed the board
from the computer as stated in the Troubleshooting section of the kpci3108.pdf
documentation. Again, the installation procedure was followed, and the same scenario
occurred. Thus we decided to call an Applications Engineer at Keithley (1-888KEITHLEY).
After downloading the 1.8 MB file that the engineer said included the correct
drivers for Windows 2000, the file was transferred and installed following the procedure
stated by the engineer from Keithley. The computer did not freeze up after installation,
however the AIO (Analog Input/Output) test panel included with DriverLinx did not
recognize the board. Again, after following the procedures to reinstall the drivers from
scratch, the test panel did not recognize the board.
We decided to call a new applications engineer at Keithley (Joey Tun). He gave
us access to the Keithley development server where the drivers we needed were located.
The drivers (found in the C:\DL_temp directory) were in the Beta testing stage and were
not guaranteed to work. They have not gone through rigorous testing and are not
available to the public at this time. However, when they are released to the pubic, it will
be advantageous to reload these drivers onto the computer to be certain there are no bugs
in the driver software. Alas, the card was recognized by the AIO test panel and was
ready for testing.
The pin layout of the corresponding analog inputs can be found on page 68
(Section 3-8) of the kpci3108.pdf documentation. We used a BK Precision function
generator to produce a test signal and a Tektronix 465 analog oscilloscope to compare the
signal to the acquired signal from the data-acquisition board. We decided to apply a
2Vp-p 30Hz signal on Terminal 15 (CH00 LO/CH08 HI) because most active brain
waves are around this frequency. Terminal 17 (AGND) was connected to earth ground
through the oscilloscope. After launching the AIO test panel, the screen capture in Figure
11 was found. The signal was found to be similar in frequency and amplitude compared
to the waveform displayed on the oscilloscope.
VirtualScope, a digital oscilloscope which was included in the software package
that came with the KPCI-3107, provides a better analysis of the signals inputted into the
board. You can display multiple signals at one time and export statistics and
measurement taken from this application. The trigger function on the VirtualScope will
prove useful for comparison of two or more signals produced by the brain or muscles.
Figure 11. AIO Test Panel with Input of 2Vp-p 30Hz.
The next step in furthering the research and development of the EEG system is to
program a driver and/or application for the board through DriverLinx and manipulate the
digital output on the KPCI-3107 board to drive an external device.
Programming the KPCI-3107 board
The DriverLINX program included with the KPCI-3107 board can be
programmed with a real-time data-acquisition device driver to specify a digital output
given a certain trigger. Examining Figure 12 below specifies a reaction via the
programmed device driver according to a trigger.
Figure 12. Example of Triggering in DriverLINX Given a Trigger Level.
The trigger level can be set in the device driver, which will be programmed through
DriverLINX. By specifying the trigger level, a digital output can be sent though the
digital output terminal of the KPCI-3107 board. Because the KPCI-3107 board has 8
differential analog inputs, 8 EEG amplifiers can be connected and analyzed at the same
time. If time permits, we will interface this into an Altera board to take advantage of the
IR capabilities it offers.
Documentation for programming the KPCI-3107 board can be found in the
C:\DrvLINX4\Docs directory on the computer with has the board installed. For our
purposes the AIOguide.pdf in this directory proved as a starting point for analog
programming a trigger for the EEG system. Knowledge of C++, Active X, or Visual
Basic can be extremely beneficial to the programmer to define and follow the necessary
initialization and procedures involved to write a C header file that can be downloaded to
the board for the specific application. However, DriverLinx comes with an object-
oriented GUI interface that allows a user inexperienced in high-level programming to
create a driver file for the data acquisition board. The program Learn DriverLinx is used
as the GUI programming interface (C:\DvrLINX4\bin\LernDL16.exe).
After launching the Learn DriverLinx program, the Device must be selected. The
“Logical Device” is usually “0”. A list of available configurations can be found in the
DriverLinx Configuration Panel. After a device is selected, the device needs to be
initialized (Device > Initialize). Next, the Analog Input must be initialized (Analog Input
> Initialize) and then the device driver is ready to be edited (Analog Input >
Edit/Execute). Descriptions of the request and events can be found from pages 16-25 in
the AIOguide.pdf documentation. This will allow the board to be programmed for a
trigger and anything else need for the application such as multiple digital clocks and data
acquisition times. These events can be programmed for each channel, analog and digital.
The documentation above also includes commands useful for programming an
application in C++. The Keithley web site has many example programs available to
reference the initialization strings and set up of the program. The documentation also
includes programming methods for application programming and on how to report errors
to the user during execution of the data acquisition.
Because of limited time constraints after the KPCI-3107 board was successfully
installed, a data application was not programmed for the EEG system. However, this
would be the next step in the process for completing the ultimate goal of driving a
mechanical device with the EEG amplifier. The applications included with the board,
such as Virtual Scope, serves as a good medium to acquire and analyze multiple brain
wave signals and compare them to each other. By understanding the signal differences, a
C++ application can be programmed with the optimal operation requirements such as
sample rate, number of channels, channel gains, and triggering levels.
Data Acquisition
After successful installation of the A/D board and the VirtualScope software,
attempts were made to measure signals from the electrodes. The black electrode lead, or
body ground, was attached to the neck while the green and red leads were placed on the
forehead. Figure 12 depicts the amplifier board with electrode leads and connections to
the breakout board.
Figure 12. Connected Amplifier board with electrode leads.
The two yellow wires, signal and analog ground, were connected to pins 15 and 17,
respectively. This corresponds to the Channel 8 analog input. A red marking on the end
of the yellow wire denotes the analog ground.
Signal acquisition was performed for baseline, two blinks, two eyebrow raises,
and jaw clinches. These can be found in Figure 13. They are similar to the oscilloscope
captures, verifying that the card and software were functioning correctly.
Error!
Baseline
0.5
0.4
0.3
0.2
0.1
0
-0.1
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
1.2
1.4
1.6
1.8
2
1.2
1.4
1.6
1.8
2
1.4
1.6
1.8
2
-0.2
-0.3
-0.4
-0.5
Two Eyebrow Raises
1
0.8
0.6
0.4
0.2
0
-0.2
0
0.2
0.4
0.6
0.8
1
-0.4
-0.6
-0.8
-1
Two Blinks
0.5
0.4
0.3
0.2
0.1
0
-0.1
0
0.2
0.4
0.6
0.8
1
-0.2
-0.3
-0.4
-0.5
Two Jaw Clinches
1.5
1
0.5
0
0
0.2
0.4
0.6
0.8
1
1.2
-0.5
-1
-1.5
Figure 12. VirtualScope Screen Captures.
Sources
Feala, J and Kane, S. Biofeedback from EEG frequency bands. Madison: U of
Chicago, 1999.
http://www.rotman-baycrest.on.ca/content/science/eegsub.html
Picton Terence W. Electroencephalograms.
http://www.cs.man.ac.uk/aig/staff/toby/research/bci/richard.seabrook.brain.computer.inte
rface.txt
The Brain Computer Interface: Techniques for controlling machines.
http://www.ece.ubc.ca/~garyb/BCI.htm
The Brain-Computer Interface Project, and links to other BCI sites.
http://www-dpmi.tu-graz.ac.at/bci.htm
BCI research information at the Graz University of Technology in Austria.
http://www.neilsquire.ca/
Homepage of the Neil Squire Foundation, an organization committed to providing
education, technology and career development for people with physical disabilities
http://www.ece.gatech.edu/academic/courses/fall2001/ece4006/Mind/group1/index.html
This is the previous neural group’s website. It contains all of their work and several
useful links under the resources heading.
http://www.ece.gatech.edu/academic/courses/spring2002/ece4006c/N1/
This is our group’s website. Links can be found under the resources and websites
headings.
www.keithley.com/
Keithley’s homepage. Information about the 3107 data acquisition card as well as the
data cables and breakout boards can be found here.
ftp://[email protected]/New%20DriverLINX%20Releases/
Developer's ftp site to download the not yet released drivers. Password: genusers