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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