BRAIN-COMPUTER INTERFACE

Lakshmi durga** Shashikar** Bhargav ram**** Luckyreddy.595@gmail.com **Samboor.shashikar@gmail.com ** bhargavseetha@gmail.com

ABSTRACT: Traditionally the brain has been under constant research and the source of numerous technologies. One such technology, brain-computer interface, is the extraction of brain signals and their use in real time applications .The brain consists of three main regions of which the cerebellum is focused on by this technology. This technology uses electrodes to extract signals from corresponding regions of the brain and use these signals to operate devices. This paper gives an idea about the brain-computer interface and its applications. Extraction of brain signals from various regions of the brain using both invasive and noninvasive techniques and the use of this signal in real time applications has been shown in this paper with the development of Brain Controlled Car (BCC), a device for persons with physical disabilities. In this paper the technology has been taken a step further with the design of the BCC that provides mobility to persons with motor immobility. The car works on the asynchronous mechanism of Artificial Intelligence. It is a great advance of technology which will make the disabled fit for doing everything a normal man does.

KEYWORDS: Brain-Computer Interface (BCI), Brain Controlled Car (BCC), Electroencephalography, Bio Control System, Electromechanical Control Unit.

1. INTRODUCTIONA brain-computer interface uses electrophysiological signals to control remote devices. Most current BCIs are not invasive. They consist of electrodes applied to the scalp of an individual or worn in an electrode cap, these electrodes pick up the brain’s electrical activity (at the microvolt level) and carry it into amplifiers such as the ones shown in Figure1.These amplifiers amplify the signal approximately ten thousand times and then pass the signal via an analog to digital converter to a computer for processing . The computer processes the EEG Signal and uses it in order to accomplish tasks such as communication and environmental control. BCIs are slow in comparison with normal human actions, because of the complexity and noisiness of the signals used, as well as the time necessary to complete recognition and signal processing. The phrase Brain-Computer Interface (BCI) when taken literally means to interface an individual’s electrophysiological signals with a computer. A true BCI only uses signals from the brain and as such must treat eye and muscle movements as artifacts or noise. On the other hand, a system that uses eye, muscle, or other body potentials mixed with EEG signals, is a brain-body actuated system. The BCI system uses oscillatory ElectroEncephaloGram (EEG) signals, recorded during specific mental activity, as input and provides a control option by its output. The obtained output signals are presently evaluated for different purposes, such as

cursor control, selection of letters or words, or control of prosthesis. People who are paralyzed or have other severe movement disorders need alternative methods for communication and control. Currently available augmentative communication methods require some muscle control. Whether they use one muscle group to supply the function normally provided by another (e.g., use extra ocular muscles to drive a peech synthesizer) .Thus, they may not be useful for those who are totally paralyzed (e.g., by Amyotrophic Lateral Sclerosis (ALS) or brainstem stroke) or have other severe motor disabilities. These individuals need an alternative communication channel that does not depend on muscle control.

Figure1: A 32-channel electrode cap (above) and a 32-channel set of analog grass amplifiers (below)

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The current and the most important application of a BCI is the restoration of communication channel for patients with locked-in-syndrome.2. STRUCTURE OF BRAIN COMPUTERARCHITECTUREThe common structure of a Brain-Computer Interface is the following:i) Signal Acquisition: the EEG signals are obtained from the brain through invasive or non-invasive methods (for example, electrodes). ii) Signal Pre-Processing: once the signals are acquired, it is necessary to clean them.iii) Signal Classification: once the signals are cleaned, they will be processed and classified to find out which kind of mental task the subject is performing.iv) Computer Interaction: once the signals are classified, they will be used by an appropriate algorithm for the development of certain applications.

Figure2: Brain computer interface

3. BCI ARCHITECTURE The processing unit is subdivided into a preprocessing unit, responsible for artifact detection, and a feature extraction and recognition unit that identifies the command sent by the user to the BCI. The output subsystem generates an action associated to this command. This action constitutes a feedback to the user who can modulate her mental activity so as to produce those EEG patterns that make the BCI accomplish her intents. Interfaces based on brain signals require on-line detection of mental states from spontaneous activity; different cortical areas are activated while thinking different things (i.e. a mathematical computation, an imagined arm movement, a music composition, etc). The information of these "mental states" can be recorded with

different methods. Neuropsychological signals can be generated by the following methods: implanted methods evoked potentialsThe following are the types of signals that the brain responds to differently:EEG OUTPUT WAVESThe analysis of continuous EEG signals or brain waves is complex, due to the large amount of information received from every electrode. As a science in itself, it has to be completed with its own set of perplexing nomenclature. Different waves, like so many Radio stations, are categorized by the frequency of their emanations and, in some cases, by the shape of their waveforms. Although none of these waves is ever emitted alone, the state of consciousness of the individuals may make one frequency range more pronounced than others. Five types are particularly important:BETA WAVES: The rate of change lies between 13 and 30 Hz, and usually has a low voltage between 5-30V BETA. The rate of change lies between 13 and 30 Hz, and usually has a low voltage between 5-30 V Beta is the brain wave usually associated with active thinking, active attention, focus on the the outside world or solving concrete problems. It can reach frequencies near 50 hertz during intense mental activity.ALPHA WAVES: The rate of change lies between 8 and 13 Hz, with 30-50 V amplitude. Alpha waves have been thought to indicate both a relaxed awareness and also in attention. They are strongest over the occipital (back of the head) cortex and also over frontal cortex. Alpha is the most prominent wave in the whole realm of brain activity and possibly covers a greater range than has been previously thought of. It is frequent to see a peak in the beta range as high as 20 Hz, which has the characteristics of an alpha state rather than a beta, and the setting in which such a response appears also leads to the same conclusion. Alpha alone seems to indicate an empty mind rather than a relaxed one, a mindless state rather than a passive one, and can be reduced or eliminated by opening the eyes, by hearing unfamiliar sounds, or by anxiety or mental concentration.THETA WAVES: Theta waves lie within the range of 4 to 7 Hz, with amplitude usually greater than 20 V. Theta arises from emotional stress, especially frustration or disappointment. Theta has been also associated with access to unconscious material, creative inspiration and

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deep meditation. The large dominant peak of the theta waves is around 7 Hz.DELTA WAVES: Delta waves lie within the range of 0.5 to 4 Hz, with variable amplitude. Delta waves are primarily associated with deep sleep, and in the waking state, were thought to indicate physical defects in the brain. It is very easy to confuse artifact signals caused by the large muscles of the neck and jaw with the genuine delta responses. This is because the muscles are near the surface of the skin and produce large signals whereas the signal which is of interest originates deep in the brain and is severely attenuated in passing through the skull. Nevertheless, with an instant analysis EEG, it is easy to see when the response is caused by excessive movement.GAMMA WAVES: Gamma waves lie within the range of 35Hz and up. It is thought that this band reflects the mechanism of consciousness - the binding together of distinct modular brain functions into coherent percepts capable of behaving in a re-entrant fashion (feeding back on them over time to create a sense of stream-of-consciousness).MU WAVES: It is an 8-12 Hz spontaneous EEG wave associated with motor activities and maximally recorded over motor cortex. They diminish with movement or the intention to move. Mu wave is in the same frequency band as in the alpha wave, but this last one is recorded over occipital cortex. Most attempts to control a computer with continuous EEG measurements work by monitoring alpha or mu waves, because people can learn to change the amplitude of these two waves by making the appropriate mental effort. A person might accomplish this result, for instance, by recalling some strongly stimulating image or by raising his or her level of attention.4. APPLICATION OF BCI - BRAIN CONTROLLED CARThe video and thermo gram analyzer continuously monitors 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).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 o f the computer. If the video Images match with the database entries then the security system advances to the next stage. Here the thermographic image verification is done with the database. Once 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. 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. 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.

Figure 3: Wheel chair car

5. BIO CONTROL SYSTEM

The bio control system integrates signals from various other sy s t e m s a nd c om pa r e s t h em w i th originals i n t he database. It comprises of the following systems:

i) Brain-computer interface

ii) Automatic security system

iii) Automatic navigation system

Now let us discuss each system in detail.

5.1. Brain – Computer Interface

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Brain-computer interface is a subsystem that 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.

Figure 4: LFASD

. Figure 5: Person operating a BCC

Figure 6: EEG Transmission

: Figure 7: Electroencephalogram

5.1.1. Test Results Comparing Driver Accuracy With/Without BCIi) Able-bodied subjects using imaginary movements could attain equal or better control accuracies than able-bodied subjects using real movements.ii) Subjects demonstrated activation accuracies in the range of 70-82% with false activations below 2%.iii) Accuracies using actual finger movements were observed in the range 36-83%iv) The average classification accuracy of imaginary movements was over 99%

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Figure 8: Brain-to- Machine Mechanism

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.

Figure 9: Eyeball Tracking

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 electromechanical actuators. The angle of turn is calibrated from the distance moved by the dot on the screen.

Figure 10: Car controlled by EPOC EEG sensor

Figure11: Electromechanical Control Unit

Figure 12: Sensors and Their Range

5.2. Automatic Security SystemThe Automatic Security System is a subsystem that will monitor 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 and waits for sometime, to continue the drive. A confirmed reply activates the program for automatic drive. If the driver is doesn’t give reply then the computer prompts the driver for the destination before the drive.5.3. Automatic Navigation SystemThe Automatic Navigation System is a subsystem that 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 r a d i o the computer drives the car automatically. Video and anti-collision sensors mainly assist this drive by providing continuous live feed of the environment up to 180 m, which is sufficient

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for the purpose.

Figure 13: EEG Analysis Window6. CONCLUSIONI would like to conclude my presentation by saying that BCI is a boon for disabled people. Even though this field of science has grown vastly in last few years we are still a few steps away from the scene where people drive brain-operated wheelchairs on the streets. New technologies need to be developed and people in the neuroscience field need also to take into account other brain imaging techniques, such as MEG and FMRI, to develop the future BCI.As time passes BCI might be a part of our every day lives. Who knows, in twenty years I will not have to type this report with my fingers, but just the conscious control of my thoughts would be enough. When all the requirements are satisfied and if this car becomes cost effective then we shall witness a 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.REFERENCES1. www. spectrum.ieee.org 2. www.mobilemag.com3. http://www.howstuffworks.com

7. BIOGRAPHY

S Bhargav ram(Email: bhargavseetha@gmail.com)III Year Kasireddy Narayan Reddy College of Engineering & Research (KNRR)

Shashikar(Email: Samboor.shashikar@gmail.com)III Year Kasireddy Narayan Reddy College of Engineering & Research (KNRR)

Lakshmi durga(Email: Luckyreddy.595@gmail.com)III Year Kasireddy Narayan Reddy College of Engineering & Research (KNRR)

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