NON INVASIVE METHODS FOR BRAIN COMPUTER INTERFACE

NON INVASIVE METHODS FOR BRAIN COMPUTER INTERFACEUnder the guidance of : Presented by: Prof. (Dr.) JYOTI SAXENA and Ramandeep Kaur Er SUKHJINDER SINGH Uni. Roll No. 1314284 M.Tech. ECE (FT)

Brain Computer InterfaceBrain Computer Interface is a direct communication pathway between brain and computer.A BCI is a technology which allows a human to control a computer, peripheral or other electronic device with thought.It does so by using electrodes to detect electric signals in the brain which are sent to a computer.The computer then translates these electric signals into data which is used to control a computer or a device linked to a computer.

Types of BCIBCI can be classified into three following categories:

Invasive BCIInvasive strategies are the brain computer interfaces which are implanted inside the brain of the subject surgically which are based on the use of array of microelectrodes implanted into visual cortex. These implants provides highest quality signals and capable to give better temporal and spatial resolution. Invasive BCI mostly provide useful functionality to paralyzed people. These can provide restoration of vision by concentrating or linking brain with external cameras and can also restore use of limbs by using brain controlled robotic legs and arms. Partial BCIThe partially invasive mechanisms are implanted inside the skull but rest outside the brain slightly within the grey matter. When we compare it with Invasive BCI it is one bit weaker than Invasive. Electrocoticography (ECoG) is example of partially Invasive BCI technique.Non Invasive BCINon-invasive strategies deal externally with the brain signals. Non-invasive BCI do not employ surgical implants. Non-invasive BCI devices are considered to be safest and are low cost. These have the slightest or least signal clarity when communicating with skull distort signals. The signals are detected by placing some electrodes at specific positions of the skull. Electroencelography (EEG), Functional magneto-resonance imaging (fMRI), Magnetoencelography (MEG), magnetic resonance imaging (MRI), computerized tomography (CT), near infrared spectroscopy (NIRS) etc. are some of the examples of non-invasive BCI.Non Invasive Methods for Brain monitoringElectroencelography (EEG)Funtional Magnetic Resonance Imaging (fMRI)Magnetoencelography (MEG)Near Infrared Spectroscopy (NIRS) Electroencelography (EEG)Electroencephalography is a non-invasive method of measuring the bioelectrical activity of the brain. Signals are acquired through electrodes placed on the surface of the scalp which detect potential changes caused by the activity of neurons of the cerebral cortex. EEG is very useful to monitor and diagnose epilepsy, sleep disorders, head trauma, brain tumours disorders of consciousness and other brain conditions.Functional Magnetic Resonance Imaging (fMRI)Functional Magneto-Resonance Imaging is a non-invasive neuroimaging technique which detects changes in local cerebral blood volume, cerebral blood flow and oxygenation levels during neural activation by means of electromagnetic fields. fMRI is generally performed using MRI scanners which apply electromagnetic fields of strength in the order of 3T or 7T. The main advantage of the use of fMRI is high space resolution. fMRI appears unsuitable for rapid communication in BCI systems and is highly susceptible to head motion artefacts.MethodologyCurrent and Future ApplicationsRehabilitation: The Rehabilitation idealize to enhance life quality of human who are paralyzed completely or partially and suffers difficulties due to Amyotrophic Lateral Sclerosis (ALS), Cranium Traumatism, Locked in Syndrome, severe cerebral paralysis etc. after losing all facilities or potentials of voluntary movements, these patients go into state where communication is impossible. These human can communicate with world and external devices.

Communication & control : BCIs provide options for communication and control for people with devastating neuromuscular disorders (such as amyotrophic lateral sclerosis, or ALS, brainstem stroke, cerebral palsy, and spinal cord injury).Gaming: The future gamers will be able to drive cars and steer just by thinking "go left" or "go right" or "brake". BCI also can be used to enjoy your home theatre by handling an entertainment system with your thoughts which will make a remote control obsolete.

Military Defence System: DARPA (Defence Advanced Research Projects Agency) has been interested in Brain-Machine-Interfaces from many years for military applications like wiring fighter pilots directly to their planes to allow autonomous flight from the safety of the ground.

AdvantagesHelp people with severe debilitating muscle wasting diseases, or with the so-called locked-in syndrome, to communicate.Help in direct brain communication in completely paralyzed patients.Help to those who suffers from disorders like ALS (Amyotrophic lateral sclerosis) , Brainstem stroke, brain or spinal cord injury.

LimitationsElectrodes: Electrodes to be attached inside the skull or even implanted in the brain. Dr James admitted that this opened up many ethical problems.Liability: Most people would agree that, under normal circumstances, we are fully responsible for our actions. However, if our intent was affected by a braincomputer interface, incorrect actions may be produced simply by incorrect detection of correct intent.Privacy: The capacity to induce information into the brain may provide us with the ability to base our actions on a better assessment of the environment. Because this information is provided by a computer, it could be accessed and modified by third parties, which may allow them to influence our actions.

Literature SurveyF Lotte, M Congedo, A Lecuyer, F Lamarche and B Arnaldi surveyed the classification algorithm used to design BCI system based on EEG. The algorithms were divided into five categories: linear classifiers, nearest neighbour classifier, combinations of classifiers, neural networks and non-linear Bayesian classifiers. The result obtained, in BCI content were analysed and compound providing readers with guidelines to choose or design a classifier for a BCI system. It seemed that SVM are greatly efficient for synchronous BCI because of their regularization property and their immunity to curse of dimensionality. For synchronous experiments, combinations of classifiers and dynamic classifiers seems to be very effective. They present that band power (BP) features to be efficient for classification of motor imagery.Mohamed Mostapa Fouad, Khalid Mohamed Amin, Nashwa EL-Bendary and Aboul Ella Hassain reviewed brain computer interfaces. A BCI system can allow the encephalic activity to control the computers and external devices such as prosthetics, BCI aims to provide aid for people buffering neuromuscular diseases as computer could permit them to perform different tasks, from accessing computer-based games to communication. Advancements in neural prosthetics have led interest in the use of BCI .the research aims at the development of devices that could be controlled by brain signals. Non-invasive method ranges were defined in the research of electroencephalogram (EEG) and electrocorticographic (ECoG) electrode array, the information regarding the state of art in neuroimaging based approaches and their applications were provided. Evaluation of different classification methodology applied to the brains captured signals was done, BCI is directed towards robot industry so that solutions could come up where robot could sense and act like human.

18Rabie A Ramadan, S Refat, Marwa AElshahed and Rasha A Ali surveyed that BCI reads the waves generated from the brain at different locations in the human head translates these into actions and commands that can control the computer. The classification of BCI is alone into three main categories depending on the way the electrical signals obtained from neuron cells in the brain. There are plenty of sig. which can be used for BCI and these signals can be divided into two categories field potentials and spikes component of particular interest to BCI could be divided into four classes which are oscillatory EEG activity, Event related potentials (ERPS), slow cortical potentials (SCP), and neural potentials. Many techniques have been employed to monitor brain activities and each technique have their own characteristics and related pros and cons.

Objective of ThesisThe objective of this dissertation is to carry out the comparison of the existing techniques associated with non-invasive brain computer interfaces. In this thesis work, EEG and fMRI techniques shall be studied for the comparison of non-invasive brain computer interfaces. The study of these techniques will provide a broader perspective in better understanding of the brain computer interface for further research in future applications.

ReferencesMinakshi and Peter Gill, (2014), Review on: Brain Computing Interface, International Journal of Engineering Research, 2(6), pp. 131-135. Garima Singh and Manju Kaushik (2012), Brain to Brain Communication: Without any Interface images, thoughts can be exchanged between minds, International Journal of Computer Science & Engineering Technology (IJCSET), 3(9), pp. 411-414.Luis Fernando Nicolas-Alonso and Jaime Gomez-Gil (2012), Brain Computer Interfaces, a Review, Sensors, 12(10), pp. 1211-1279.

21Remigiusz J.Rak, Marcin Kolodeziej and Andrej Majkowski (2012), Brain-Computer Interface as Measurement and Control System the Review Paper, Metrology andMeasurement Systems, 19(3), pp. 427-444.Tushar Kanti Bera (2015), Non-invasive Electromagnetic Methods for Brain Monitoring : A Technical Review, Springer International Publishing, Switzerland, 74(3), pp. 51-95. Rabie A. Ramadan, S. Refat, Marwa A. Elshahed and Rasha A. Ali (2015), Basics of Brain Computer Interface, Springer International Publishing, Switzerland, 74(3), pp. 31-50.Mohamed Mostafa Fouad, Khalid Mohamed Amin, Nashwa El-Bendary and Aboul Ella Hassanien (2015), Brain Computer Interface: A Review, Springer International Publishing, Switzerland, 74(3), pp. 3-30.F. Lotte, M. Congedo, A. Lcuyer, F. Lamarche, and B. Arnaldi (2007), A review of Classification algorithms for EEG-based brain-computer interfaces, Journal of Neural Engineering, 4(7),pp. 1-13.

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