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CHAPTER-1 INTRODUCTION The video and thermo gram analyzer continuously monitor activities outside the car. A brain-computer interface (BCI), sometimes called a direct neural interface or a brain-machine interface, is a direct communication pathway between a human or animal brain (or brain cell culture) and an external device. In one-way BCIs, computers either accept commands from the brain or send signals to it (for example, to restore vision) but not both. Two-way BCIs would allow brains and external devices to exchange information in both directions but have yet to be successfully implanted in animals or humans. In this definition, the word brain means the brain or nervous system of an organic life form rather than the mind. Computer means any processing or computational device, from simple circuits to silicon chips (including hypothetical future technologies such as quantum computing). Figure 1.1 Brain Controlled Car Once the driver (disabled) nears the car. The security system of the car is activated. Images as well as thermo graphic results of the driver are previously fed into the database of the computer. If the video images match with the database entries then the security system advances to the next stage. Here the thermo graphic image verification is done with the database. 1 After the driver passes this stage the door slides to the sides and a ramp is lowered from its floor. The ramp has flip actuators in its lower end. Once the driver enters the ramp, the flip actuates the ramp to be lifted horizontally as shown in Figure 1.1. Then robotic arms assist the driver to his seat. As soon as the driver is seated the EEG (electroencephalogram) helmet, attached to the top of the seat, is lowered and suitably placed on the driver’s head as shown in the Figure1.2. Figure1.2 : EEG Helmet A wide screen of the computer is placed at an angle aesthetically suitable to the driver. Each program can be controlled either directly by a mouse or by a shortcut. For starting the car, the start button is clicked. Accordingly the computer switches ON the circuit from the battery to the A.C Series Induction motors. 2 CHAPTER-2 BIO-CONTROL SYSTEM The Biocontrol system integrates signals from various other systems and compares them with originals in the database. It comprises of the following systems: Brain-computer interface Automatic security system Automatic navigation system Now let us discuss each system in detail. 2.1 Brain – Computer interface: Brain computer interface It sometimes called a direct neural interface or a brainmachine. It is a direct communication pathway between a human and an external device. Two-way BCIs would allow brains and external devices to exchange information in both directions. Brain-computer interfaces will increase acceptance by offering customized, intelligent help and training, especially for the non-expert user. Development of such a flexible interface paradigm raises several challenges in the areas of machine perception and automatic explanation. The teams doing research in this field have developed a single-position, brain-controlled switch that responds to specific patterns detected in spatiotemporal electroencephalograms (EEG) measured from the human scalp. We refer to this initial design as the Low-Frequency Asynchronous Switch Design (LF-ASD) as shown in Figure 2.1. Figure 2.1 LF-ASD 3 The EEG is then filtered and run through a fast Fourier transform before being displayed as a three dimensional graphic. The data can then be piped into MIDI compatible music programs. Furthermore, MIDI can be adjusted to control other external processes, such as robotics. The experimental control system is configured for the particular task being used in the evaluation. Real Time Workshop generates all the control programs from Simulink models and C/C++ using MS Visual C++ 6.0. Analysis of data is mostly done within Mat lab environment. 2.1.1 EEG Transmission: EEG Transmission EEG (electroencephalogram) A wide screen of the computer is placed at an angle aesthetically suitable to the driver. Each program can be controlled either directly by a mouse or by a short cut. The EEG transmission is shown in the Figure 2.2. Figure 2.2 EEG Transmission 2.1.2 Features of EEG band Remote analysis data can be sent and analyzed in real-time over a network or modem connection Data can be fully exported in raw data, FFT & average formats. Ultra Low noise balanced DC coupling amplifier. Max input 100microV p-p, minimum digital resolution is 100 micro V p-p / 256 = 0.390625 micro V p-p. FFT point can select from 128 (0.9375 Hz), 256 (0.46875 Hz), 512(0.234375 Hz resolution). 4 Support for additional serial ports via plug-in boar; allows extensive serial input & output control. Infinite real-time data acquisition (dependent upon hard drive size). Real-time 3-D & 2-D FFT with peak indicator, Raw Data, and Horizontal Bar displays with Quick Draw mode. Full 24 bit color support; data can be analyzed with any standard or user. Customized color palettes; color cycling available in 8 bit mode with Quick Draw mode. Interactive real-time FFT filtering with Quick Draw mode. Real-time 3-D FFT (left, right, coherence and relative coherence), raw wave, sphere frequency and six brain wave switch in one OpenGL display. Full Brainwave driven Quick Time Movie, Quick Time MIDI control; user configurable Full Brain wave driven sound control, support for 16 bit sound; user configurable Full image capture and playback control; user configurable. Table 2.1 Sequence of operations that convert Scalp EEG Activity into Cursor Movement Device Input Operation Output EEG amplifier Scalp voltages(µV) Amplification Analog voltages(V) A/D board Analog Voltages Digitization Digitized voltages DSP board Digitized voltages Spatial filtering EEG cursor-control channels DSP board EEG cursor-control Spectral analysis Control signals Transformation Cursor movements channels PC Control signals 22.1.3 Test results Comparing driver accuracy with/without BCI: 5 Able-bodied subjects using imaginary movements could attain equal or better control accuracies than able-bodied subjects using real movements. Subjects demonstrated activation accuracies in the range of 70-82% with false activations below 2%. Accuracies using actual finger movements were observed in the range 36-83%. The average classification accuracy of imaginary movements was over 99%. 2.1.4 Brain to Machine Interface: Figure 2.4 Brain-to- Machine Mechanism The block diagram for Brain to Machine Interface is shown in the above Figure 2.4. The principle behind the whole mechanism is that the impulse of the human brain can be tracked and even decoded. The Low-Frequency Asynchronous Switch Design traces the motor neurons in the brain. When the driver attempts for a physical movement, he/she sends an impulse to the motor neuron. These motor neurons carry the signal to the physical components such as hands or legs. Hence we decode the message at the motor neuron to obtain maximum accuracy. By observing the sensory neurons we can monitor the eye movement of the driver. 6 Figure 2.5 Eyeball Tracking The eye ball Tracking is shown in the above Figure 2.5. As the eye moves, the cursor on the screen also moves and is also brightened when the driver concentrates on one particular point in his environment. The sensors, which are placed at the front and rear ends of the car, send a live feedback of the environment to the computer. The steering wheel is turned through a specific angle by electro mechanical actuators. The angle of turn is calibrated from the distance moved by the dot on the screen. The Electro mechanical Control Uint is shown in the Figure2.6 Figure 2.6 Electro mechanical Control Unit 7 Figure 2.7 Sensors and Their Range 2.2 Automatic Security System: The EEG of the driver is monitored continually. When it drops less than 4 Hz then the driver is in an unstable state. A message is given to the driver for confirmation to continue the drive. A confirmed reply activates the program automatic drive. The computer prompts the driver for the destination before the drive. 2.3 Automatic Navigation System: As the computer is based on artificial intelligence it automatically monitors every route the car travels and stores it in its map database for future use. The map database is analyzed and the shortest route to the destination is chosen. With traffic monitoring system provided by xm satellite radio the computer drives the car automatically. Video and anticollision sensors mainly assist this drive by providing continuous live feed of the environment up to 180 m, which is sufficient for the purpose. 8 CHAPTER-3 WORKING OF EEG EEG is the recording of electrical activity in the brain. The activity is recorded by placing electrodes on the skull over electrically active tissue and measuring the voltage across these electrodes. The electrical activity arises from the firing of many neurons in the brain. There is always a “background” firing of neurons, although the pattern changes when the subject under study is active. One area of interest is the study of waves: these are oscillations in the EEG that are grouped into four different frequency bands: Figure 3.1 : Time Series plot of EEG data Alpha waves have frequencies ranging from 8 to 13Hz. They are usually present in a normal human at rest with closed eyes and not subjected to any external stimulus. They can be blocked by stimulation or external activity. Beta waves have frequencies ranging from 14 to 30Hz and mainly occur when the subject is exposed to an external stimulus; they are not as strongly periodic as the alpha waves. Theta waves have frequencies ranging from 3 to 8Hz. They are present in the EEG of new borns or adults who have disease or injury. Delta waves are as theta waves except their frequency range is 0.5-3.5Hz. 9 3.1 DESIGN: Driving the vehicle is only one of the many tasks drivers need to focus on. As such, a BCI powered interaction method could relieve the cognitive workload of the driver and reduce the level of distractions introduced by manually controlling multiple in-vehicle systems. Figure3.2 Block Diagram of EEG Description of EEG Control Figure 3.3 shows scalp topographies generated off-line from 64 channels of EEG data collected during performance of one-dimensional cursor control by one well trained user. The control signal was the sum of the voltages at 10Hz at two locations centered over right and left sensorimotor cortices, respectively. Figure 3.3A shows topographies of the mean 10-Hz voltage computed when the target was at the top or bottom edge of the screen. Mean 10-Hz voltages over both sensorimotor cortices are much greater during top targets than during bottom targets. Voltage is highest on the right side. Figure 3.3B shows the 10-Hz topography of y2. This measure can be considered an index of the signal-to-noise ratio at each location. Like the voltage difference evident in A, the value 10 of y2 is greatest over the sensorimotor cortices. However, in contrast to A, the largest value of y2 is over the left side. These topographical displays illustrate the typically narrow spatial focus of a well-trained user's EEG control. Figure 3.3 Topographical analysis of four sessions from a well-trained user (A) Average voltages at 10 Hz for top and bottom targets. (B) Values of r 2 for the top/bottom difference. 3.2 Controlling In-Vehicle Systems with a Commercial EEG Headset During the experiment, the participants were involved in executing a set of activities that are always or at least sometimes present in everyday driving. The activities were grouped in the following three categories: Driving task (highest priority) – firstly, every driver has to be in control of the vehicle and obey the traffic rules. 11 Search task (high priority) – drivers are sometimes involved in search-and-find tasks in the environment surrounding the vehicle (e.g., finding a parking lot, finding a store, etc.) Control task (low priority) – drivers have to interact with the dashboard of the vehicle (in our case via BCI) to activate various devices and vehicle subsystems (e.g., lights, radio, GPS, etc.) Figure 3.4 Development of EEG control following the appearance of target Figure 3.4 illustrates, with data from a well-trained user, the timing of the development of EEG control in response to appearance of a top or bottom target. It plots the control signal (i.e., 10-Hz voltage over left sensorimotor cortex) that controlled cursor movement on-line. Average values are shown for every 100-msec time segment from the first appearance of the target until the cursor started moving. It is clear that the user's response to the target develops within the first second after the appearance of the cursor. This information is important for efforts to maximize the rate and accuracy of cursor control by modifying trial timing. 12 CHAPTER-4 CONCLUSION When the above requirements are satisfied and if this car becomes cost effective then hope that revolutionary change in the society where the demarcation between the abler and the disabled vanishes. Thus the integration of bioelectronics with automotive systems is essential to develop efficient and futuristic vehicles, which shall be witnessed soon helping the disabled in every manner in the field of transportation. 13 References: [1] Flotzinger, D. Kalcher, J.Wolpaw, J.R., McFarland, J.J. and Pfurtscheller, ”Off-line Classification of EEG from the New York Brain-Computer Interface (BCI)" Graz University of Technology, Austria 1993. [2] Keirn, Z.A. and Aunon, J.I.," Man-Machine Communications through Brain- Wave Processing IEEE Engineering in Medicine and Biology Magazine, March 1990. [3] Sutter, E.E., "The brain response interface: communication through visually-induced electrical brain responses" Journal of Microcomputer Applications, 1992, 15: 31-45. [4] www.seminarsforyou.com 14