Brain computer interface seminar report pdf

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A SEMINAR REPORT ON BRAIN COMPUTER INTERFACE SESSION Submitted to- Submitted By Prachi Parashar Rahul Sharma (Assistant Professor- . Department of Computer Science and Engineering 4 BRAINGATE TECHNOLOGY SEMINAR REPORT aracer.mobitic Architecture of BCI Brain Computer. Brain Computer Interface, Ask Latest information, Abstract, Report, Presentation ( pdf,doc,ppt),Brain Computer Interface technology discussion,Brain Computer.

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Brain Computer Interface Seminar Report Pdf

BRAIN COMPUTER INTERFACEAbstract: Brain–computer interfaces (BCIs) enable users to control devices withelectroencephalographic. Report by Mr. Alan Wong. This seminar on Brain Computer Interfaces was held on 25 March The speaker of this seminar was Professor Bertram Shi of the . Seminar report and PPT on Brain-Computer Interface BCI gives the basic Download the PDF seminar report, documents and PPT Learn all.

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The Donchin BCI is based on the presentation of a 6x6 letter matrix, in which in short intervals, one of the rows or one of the columns of the matrix is flashed. A BCI can also be realized based on the evaluation of the amplitude of steady state VEPs induced by flickering lights. When the user focuses attention to one of more flicking lights the corresponding amplitude becomes enhanced.

The most immediate and practical goal of Brain Computer Interface research is to create a mechanical output from neuronal activity. The challenge of Brain Computer Interface research is to create a system that will allow patients who have damage between their motor cortex and muscular system to bypass the damaged route and activate outside mechanisms by using neuronal signals.

This would potentially allow an otherwise paralyzed person to control a motorized wheelchair, computer pointer, or robotic arm by thought alone. Fig 4. A brain actuated wheelchair. Neuroprosthetic device using a The subject guides the wheelchair Brain Computer Interface.

Most Brain Computer Interfaces translate neural activity into a continuous movement command, which guides a computer cursor to a desired visual target.

If the cursor is used to select targets representing discrete actions, the Brain Computer Interface serves as communication prosthesis. Examples include typing keys on a keyboard, turning on room lights, and moving a wheelchair in specific directions. Visual attention, however, might be needed for application control to drive a wheelchair, to observe the environment, etc.

Feedback plays an important role when learning to use a Brain Computer Interface. How can brainwaves directly control external devices? For humans, however, noninvasive approaches avoid health risks and associated ethical concerns.

Simulation of the Fig. Left Active brain areas. Upper right Extracted brain activity patterns. Lower right Pattern classification processing. Thus, EEG signals suffer from a reduced spatial resolution and increased noise when measurements are taken on the scalp. Consequently, current EEG-based brain-actuated devices are limited by low channel capacity and are considered too slow for controlling rapid and complex sequences of robot movements.

Recently, researchers had shown for the first time that online EEG signal analysis, if used in combination with advanced robotics and machine learning techniques, is sufficient for humans to continuously control a mobile robot and a wheelchair. An evoked BCI exploits a strong characteristic of the EEG, the evoked potential, which reflects the immediate automatic responses of the brain to some external stimuli. In principle, evoked potentials are easy to detect with scalp electrodes. However, evoking them requires external stimulation, so they apply to only a limited task range.

Spontaneous BCIs are based on the analysis of EEG phenomena associated with various aspects of brain function related to mental tasks that the subject carries out at will. In such asynchronous protocols, the subject can deliver a mental command at any moment without waiting for external cues. The user and the BCI are coupled together and adapt to each other. In other words, we use machine learning approaches to discover the individual EEG patterns characterizing the mental tasks users execute while learning to modulate their brainwaves in a way that will improve system recognition of their intentions.

We use statistical machine learning techniques at two levels: Incorporating rejection criteria to avoid making risky decisions is an important BCI concern. How is it possible to control a robot that must make accurate turns at precise moments using signals that arrive at a rate of about one bit per second? The subject delivers a few high-level mental commands and the robot executes these commands autonomously using the readings of its onboard sensors. This approach makes it possible to continuously control a mobile robot— emulating a motorized wheelchair—along nontrivial trajectories requiring fast and frequent switches between mental tasks.

For brain-actuated robots, in contrast to augmented communication through BCI, fast decision making is critical. Real-time control of brain-actuated devices, especially robots and neuro prostheses, is the most challenging BCI application. A first line of research is online adaptation of the interface to the user to keep the BCI constantly tuned to its owner.

In addition, brain signals change naturally over time. In particular, they can change from one session that supplies the data to train the classifier to the next session that applies the classifier. The most widely used neuroprosthetic device is the cochlear implant, which was implanted in approximately , people worldwide as of There are also several neuroprosthetic devices that aim to restore vision, including retinal implants, etc.

The differences between Brain Computer Interfaces and Neuroprosthetics are mostly in the ways the terms are used: Neuroprosthetics typically connect the nervous system, to a device, whereas Brain Computer Interfaces usually connect the brain or nervous system with a computer system. The terms are sometimes used interchangeably and for good reason. Neuroprosthetics and Brain Computer Interface seek to achieve the same aims, such as restoring sight, hearing, movement, ability to communicate, and even cognitive function.

Both use similar experimental methods and surgical techniques. Cyberkinetic Neurotechnology Inc, markets its electrode arrays under the BrainGate product name and has set the development of practical Brain Computer Interfaces for humans as its major goal. Neural Signals was founded in to develop Brain Computer Interfaces that would allow paralyzed patients to communicate with the outside world and control external devices. As well as an invasive Brain Computer Interface, the company also sells an implant to restore speech.

Neural Signals' Brain Communicator Brain Computer Interface device uses glass cones containing microelectrodes coated with proteins to encourage the electrodes to bind to neurons.

Avery Biomedical Devices and Stony Brook University are continuing development of the implant, which has not yet received FDA approval for human implantation. The Audeo is being developed to create a human-computer interface for communication without the need of physical motor control or speech production.

Using signal processing, unpronounced speech representing the thought of the mind can be translated from intercepted neurological signals. Mindball is a product developed and commercialized by Interactive Productline in which players compete to control a ball's movement across a table by becoming more relaxed and focused.

Interactive Productline is a Swedish company whose objective is to develop and sell easy understandable EEG products that train the ability to relax and focus. The evolution of the Brain Computer Interface may seem to be rooted in the internal keyboard and its recent traveling companion, the mouse, but much work is being done in the areas of virtual worlds, voice recognition, handwriting recognition and gesture recognition to give us a new paradigm of computing.

It now appears we are on the edge of another brave new virtual world— the direct interface between the brain and the computer is here. The first Brain Computer Interface System will enable the composition and sending of messages, and control of a computer game.

In several research projects, patients have used the device to successfully produce control signals to select letters and words or to control specific functions of a wheelchair or prosthetic device. The activity of the brain is recorded with an EEG Electroencephalogram electrodes mounted onto the surface of the head. Guger Technologies has developed a sophisticated biosignal amplifier which allows the acquisition of the signals with very high accuracy.

The amplifier is plugged into a USB port of the notebook for signal acquisition. The big advantage of the ECoG recordings is the better signal quality. Even a single electrode overlaying a specific brain region can generate a reliable control signal for a Brain Computer Interface BCI system.

On the surface of the head the Electroencephalogram EEG measures the activity of millions of neuron to extract the control signal. An implantable, Brain Computer Interface, has been clinically tested on humans by American company Cyberkinetics. The technology driving this breakthrough in the Brain Machine Interface field has a myriad of potential applications, including the development of human augmentation for military and commercial purposes.

The BrainGate Neural Interface Device is a BCI that consists of an internal neural signal sensor and external processors that convert neural signals into an output signal under the users own control.

The sensor consists of a tiny chip with one hundred electrode sensors each that detect brain cell electrical activity. The chip is implanted on the surface of the brain in the motor cortex area that controls movement. The computers translate brain activity and create the communication output using custom decoding software. This new BCI technology has enabled the decoding of natural brain activity and the use of the extracted data for the near real-time operation of a robot without an invasive incision of the head and brain.

This breakthrough facilitates greater possibilities for new types of interface between machines and the human brain. By utilizing such methods, it is expected that the same result could be achieved with less time lag and more compact BMI system devices.

Specific signals generating paper-rock-scissors movements are extracted and decoded by a computer program, and the decoded information is transferred to a hand-shaped robot to simulate the original movement performed by the subject. While conventional machine-interfaces are operated using button switches controlled by human hands or feet, BCI uses brain activity measured by various devices and allows non-contact control of the terminal machines.

Implanted electrode arrays, and brain waves have been commonly used. In conventional BMI research efforts led by U. If advanced non- invasive BCI becomes available, users will be free from the physical burden of a surgical procedure.

This research accomplishment demonstrates the possibility of such a useful application. The new BMI technology is different in that natural brain activity associated with specific movements can be decoded without using alternative brain activity.

Hitachi's new neuro-imaging technique allows its operator to switch a train set on and off by thought alone, and the Japanese company aims to commercialize it within five years. And this all comes hot on the heels of a revolution in microsurgery, allowing artificial limbs to be wired to the brain by reusing existing nerves.

Hitachi's system doesn't invasively co-opt the nervous system, instead using a topographic modeling system to measure blood flow in the brain, translating the images into signals that are sent to the controller. So far, this new technique only allows for simple switching decisions, but Hitachi aims to commercialize it within five years for use by paralyzed patients and those undergoing "cognitive rehabilitation.

It can also be used for data acquisition, stimulus presentation, and brain monitoring applications.

BCI supports a variety of data acquisition systems, brain signals, and study or feedback paradigms. BCI also includes several tools for data import or conversion e. BCI also facilitates interactions with other software. For example, Matlab scripts can be executed in real-time from within BCI, or BCI filters can be compiled to execute as stand-alone programs.

Furthermore, a simple network-based interface allows for interactions with external programs written in any programming language. For example, a robotic arm application that is external to BCI may be controlled in real time based on brain signals processed by BCI, or BCI may use and store along with brain signals behavioral-based inputs such as eye-tracker coordinates.

Also available are the full source code and all executables, which run on most current PCs running Microsoft Windows. The complete source code is provided for the BCI system. BCI V3. An electrode-covered hat can translate brain waves into computer commands, a non- invasive thought decoder that could someday let the disabled communicate by using their brains alone, according to a new study.

The hat may someday also be used to operate word processing programs or control movement of a robotic prosthesis. It looks sort of like a light-weight elastic version of an old-fashioned rubber swimming cap, with small metal disks that are connected by a ribbon cable to EEG amplifiers and the computer.

Brain activity can be detected from the scalp, from the cortical surface, or from within the brain itself. Some devices are implanted into the brain, but the cap is noninvasive and poses minimal, if any, risk to the wearer. The problem with such caps in the past is that, they pick up all sorts of brain waves, to the point where the desired ones are lost or reduced to a quiet buzz amongst the din. It also has an enhanced decoder that not only conveys the user's intent to the computer, but also focuses on thought patterns determined to be successful in operating the computer.

As a result, the device becomes easier for the wearer to use over time. The nervous system has tremendous ability to adapt to new needs. It is possible that areas of sensorimotor cortex deprived of their normal function might conceivably acquire a new function, such as EEG electroencephalographic -based cursor control, more readily.

Once such devices are made available, they will profoundly improve lives of some individuals whose thoughts and desires are otherwise locked within their bodies. Both invasive and noninvasive BCI's will be beneficial to patients. A few Brain Computer Interface research and development projects envisioned healthy subjects as end users.

Recently, the cyberpunk movement has adopted the idea of "jacking in", sliding "biosoft" chips into slots implanted in the skull Gibson, W. Although such biosofts are still science fiction, there have been several recent steps toward interfacing the brain and computers.

Chief among these are techniques for stimulating and recording from areas of the brain with permanently implanted electrodes and using conscious control of EEG to control computers.

Some preliminary work is being done on synapsing neurons on silicon transformers and on growing neurons into neural networks on top of computer chips. The most advanced work in designing a brain-computer interface has stemmed from the evolution of traditional electrodes.

There are essentially two main problems, stimulating the brain input and recording from the brain output. Traditionally, both input and output were handled by electrodes pulled from metal wires and glass tubing. Using conventional electrodes, multi-unit recordings can be constructed from mutlibarrelled pipettes.

In addition to being fragile and bulky, the electrodes in these arrays are often too far apart, as most fine neural processes are only. Pickard describes a new type of electrode, which circumvents many of the problems listed above.

Because of these requirements the larger part of the total neuronal activity remains invisible forEEG measurement.

The brainCombining about billion neurons results in what is called the human brain.

The brain consists of the following elementsThe brainstem is an important relay station. It controls the reflexes and automatic functions, likeheart rate and blood pressure and also sleep control. The Cerebellum integrates information about position and movement from the vestibular system to coordinate limb movement and maintaining equilibrium.

The Cerebrum or cerebral cortex receives and integrates information from all of the sense organs and controls the motor functions. Furthermore it contains the higher cerebral functions like: Emotions are also processed in the cerebrum. The cortex of the cerebrum is part of the brain which is of the most interest for BCI. It isresponsible for the higher order cognitive tasks and is near the surface of the scalp. In additionthat functionality in the brain appears to be highly local. The cerebrum is divided into two hemispheres, left and right.

The left halve senses andcontrols the right half of the body and vice versa. Each hemisphere can be divided into fourlobes, the frontal, the parietal, the occipital and the temporal see figure 2. The cortex can alsoby divide in certain areas each of which is specialized for a different function. Especially thesensorimotor cortex is important for BCI. Over this part the entire human body is represented. The size of area corresponds with the importance and complexity of movement of that particularbody Homunculus: In the light of BCI it is important to know in advance in which area to search for activity bothspatially and in the frequency domain.

Brain activity measurementTo measure activity in the brain, several different approaches can be applied. Because differentphenomena can be measured in different ways: Here follows a list of the most commonly used methods see figure 2. EEG, Electroencephalography involves recording the very weak electrical field generated by action potentials of neurons in the brain using small metal electrodes.

The advantages of EEG are the high temporal resolution and the possibility of non-invasive measurement.

Low spatial resolution, caused by spatial smearing of the skull and high variability in the EEG signal are disadvantages. ECoG, Electrocorticography involves the electrophysiology of extra-cellular currents. Has both high temporal as good spatial resolution. It is a form of invasive EEG where electrodes are placed directly on the brain.

This technique is invasive and therefore requires expensive surgery and comes with significant safety risks for the patient. PET, Positron Emission Tomography indirectly measures metabolism on a cellular levelby tracking injected radioactive isotopes.

It is based on the principle that in areas ofincreased activity the metabolism is on a higher level and more isotopes are supplied bythe blood flow.

This knowledge can be used to determine which areas are generatingactivity. Good spatial resolution is an advantage of PET.

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The really bad temporalresolution about 2 minutes is a distinct disadvantage. This is due to the fact thatmetabolism is a relatively slow process. Moreover ionizing radiation makes this methodharmful for the human body and thus unusable for applications like BCI. MEG, magneto encephalography directly measures the cortical magnetic fields producedby electrical currents. This method is non-invasive and has good spatial and temporalresolution.

The real-time properties for analysis are poor. The advantages are good spatialresolution and the non-invasiveness.

But the temporal resolution is poor about onesecond and this method requires very expensive equipment and procedures. NIR, Near-infrared light penetrates the human head to sufficient depths to allowfunctional mapping of the cerebral cortex. Changes in tissue oxygenation causemodulation of absorption and scattering of photons, which can be measured [12].

NIRcan measure two responses. This response has a good temporal resolution, but is not yet feasible. To date the only experimental setup that has been used uses the slow response, which hasa poor temporal resolution. Advantages of optical techniques: Overall this lookslike a promising technique. EEG 1.

Selecting a measurement method — Why EEG? The best brain measurement method would have a high spatial and temporal resolution, be verycheap, portable and easy to apply non-invasively.

Brain Computer Interface

This method does not yet exists. The prime reason for this is the excellent temporal resolution which is a necessity for real-time BCI. And although the spatial data resulting from EEG is often distorted and far fromperfect, EEG offers direct functional correlation of brain activity. Another major plus is the ease of applying this method. With a cap containing only a fewelectrodes measurements can start. For practical uses and applications it is small and relativelyportable, which improves prospects of future applications Aside from the ease of appliance, thisis also a relatively low-cost method, certainly compared to methods like PET, MEG or MRI,which require expensive equipment and skilled professionals to operate.

Subject one with EEG cap. Although EEG is the most commonly used, this does not mean that others methods arenot feasible. With the continuous improvement of the techniques involved, they can become aviable option in the future; like for instance Near Infrared measurement.

Theelectrode is placed on the scalp. The obvious advantage is that it can be safely applied to anyoneat any moment without a lot of preparation. The second variant is the invasive EEG.

Brain Computer Interface Full Seminar Report

Instead of attaching the electrode on the skull, itis placed inside. The advantage of this variant is the higher spatial resolution obtained by it. Withnon-invasive EEG, the skull causes significant spatial smearing of the measured activity: Invasive electrodes offer the possibility to locate activity far more precise.

The obviousdrawback is that surgery is required to implant the electrodes. This comes with safety risks andhigh costs compared to non-invasive EEG. For application there must be large gain from theincreased accuracy to validate invasive EEG on human subjects.

Brain Gate Dummy unit illustrating the design of a Brain Gate interface Brain Gate is a brain implantsystem developed by the bio-tech company Cyberkinetics in in conjunction with theDepartment of Neuroscience at Brown University. The device was designed to help those whohave lost control of their limbs, or other bodily functions, such as patients with amyotrophiclateral sclerosis ALS or spinal cord injury. The computer chip, which is implanted into thebrain, monitors brain activity in the patient and converts the intention of the user into computercommands.

Currently the chip uses hair-thin electrodes that sense the electromagneticsignature of neurons firing in specific areas of the brain, for example, the area that controls armmovement. The activities are translated into electrically charged signals and are then sent anddecoded using a program, which can move either a robotic arm or a computer cursor. Accordingto the Cyberkinetics website, three patients have been implanted with the Brain Gate system.

The company has confirmed that one patient Matt Nagle has a spinal cord injury, whilstanother has advanced ALS.

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