Applications and delta (<3.5hz). These waves have certain

Applications of EEG signal Acquisition-A

Dr.S.Maheswari M.E., Ph.D1.,
,G.Prabhakaran2, R.Vignesh3, S.Vignesh4


2,3,4UG  Student, Electrical and Electronics
Engineering Department, Kongu Engineering College

[email protected]

[email protected]

[email protected]


Abstract: The Electroencephalographic (EEG) signals are those which
records the electrical status of the human brain. The signal
pattern varies according to the chemical reaction in the human
brain.  The recorded waveforms reflect the cortical
electrical activity. EEG activity is quite small, measured in
micro volts (mV). Acquisition of the EEG signal can be
used in many research fields. Depending on the frequency of obtained
signal it can be classified into five types they are: delta, theta, alpha and

EEG signals, micro volts, acquisition, research fields.


brain is the most complex part in the human body. It
controls the overall activity of our body, it
consist of billions of neurons which communicates with each
other in achieving it. Inside the human brain there will be a lot of chemical
reaction taking place in it. The evolution of the EEG signals took place in
1929. Hans Berger was the first scientist to tell about the EEG signals and on
the later stages it had found a drastic improvement in the research about the
various fields. The different types of brain waves have specific frequency such
as beta(14-30hz), alpha(8-13hz), theta(4-7hz) and delta
(<3.5hz). These waves have certain characteristics, by analysing those wave pattern we can do various research in different fields such as BCI (Brain computer interface), Psychological factors, neuro-imaging, etc Fig 1.Different kinds of brain waves II.    Neuromarketing "Acquistition of EEG signals led to the new field of science called neuromarketing.It is a new way to get the feedback that you could measure with a consumer device to discover which types of advertising are effective and useful, and which types are embrassing. Neuromarketing researchers believe that  consumers' decisions are made in a split second,  those decisions are made subconsciously. They strongly believe that decision of consumers are not factual and they are totally taken in a matter of seconds by simple attraction that the company advertises. The function of neuromarketing is to analyse how the customers emotions are triggered depending on the advertisement they see, how their sub concious mind react to it. The data it generates is extremely useful for the companies to develop an advertisement which attracts the customers they target. The data is gathered by monitoring certain biometrics, including: Eye tracking Facial coding Galvanic skin response and electro dermal activity Electroencephalography (EEG) Some neuromarketing research is conducted using fMRI, which measures brain activity by detecting changes in blood flow in response to stimuli. It yields accurate data, but it is challenging for the following reasons: It requires subjects to lie completely still in a large MRI chamber, which can be a total discomfort to the subjects. Stimuli cannot be encountered in the same way the test subject would usually be exposed to it—you can't take an MRI chamber into a retail store. It takes a lot of time and its also expensive strategy. EEG technique, on the other hand, allow neuromarketing research to be conducted efficiently  from anywhere. This methodology enables researchers to measure consumer response to an testing environment , such as a movie theatre, bar, or retail store. Small biosensors can be placed at distinct places on the head, allowing for accurate measurement of brain activity while giving the test subject full range of motion and ensuring their comfort.Changes in the electrical state of the brain can be interpreted to determine if a test subject is engaged with a piece of advertising, experiencing an emotional response, or simply not paying attention.In a focus group, a consumer may say a 30 second commercial was effective at holding their attention. An EEG reading may reveal that the same consumer was very engaged for the first ten seconds of the commercial, lost interest for the next ten, and then paid attention again for the final third of the video. Improving the middle third of the video based on this feedback could help create an even more effective commercial.Neuromarketing helps marketers create more effective and engaging advertisements. This not only benefits the brand, but also the audience—people are exposed to hundreds (if not thousands) of ads per day, so creating more informative, emotionally rewarding, and useful ads can enhance a customer's experience with a product or brand long before they consider buying. III.  Brain computer interface A.    Introduction to BCI   The major field where the EEG signals can be effectively utilised is the Brain Computer interface (BCI)."A brain–computer interface is a communication system that does not depend on the brain's normal output pathways of peripheral nerves and muscles." It reflects the principal reason for the interest in BCI development—the possibilities it offers for providing new augmentative communication technology to those who are paralysed or have other severe movement deficits. All other augmentative communication technologies require some form of muscle control, and thus may not be useful for those with the most severe motor disabilities, such as late-stage amyotrophic lateral sclerosis, brainstem stroke, or severe cerebral palsy. Therefore by using this technique we can make a lot paralysed patients to act on their own without depending on anyone.   Fig 2.Process involved in creating a BCI     B.Essential Features of BCI   BCI operation depends on the interaction of two adaptive controllers, the user's brain, which produces the activity measured by the BCI system, and the system itself, which translates that activity into specific commands. Successful BCI operation is essentially a new skill, a skill that consists not of proper muscle control but rather proper control of EEG (or single-unit) activity. Each BCI uses a particular algorithm to translate its input (e.g., its chosen EEG features) into output control signals. This algorithm might include linear or nonlinear equations, a neural network, or other methods, and might incorporate continual adaptation of important parameters to key aspects of the input provided by the user. BCI outputs can be cursor movement, letter or icon selection, or another form of device control, and provides the feedback that the user and the BCI can use to adapt so as to optimize communication. In addition to its input, translation algorithm, and output, each BCI has other distinctive characteristics. These include its On/Off mechanism (e.g., EEG signals or conventional control); response time, speed and accuracy and their combination into information transfer rate; type and extent of user training required, appropriate user population; appropriate applications; and constraints imposed on concurrent conventional sensory input and motor output (e.g., the need for a stereotyped visual input, or the requirement that the user remain motionless).   C.Matching BCI and input to the User   Matching the user with his or her optimal BCI input features is essential if BCI's are ever to be broadly applied to the communication needs of users with different disabilities. Most BCI systems use EEG or single-unit features that originate mainly in somatosensory or motor areas of cortex. These areas may be severely damaged in people with stroke or neurogenerative disease. Use of features from other CNS regions may prove necessary. For EEG-based BCI's, comprehensive multielectrode recording, performed initially and then periodically, can reveal changes in the user's performance and/or the progression of disease, and can thereby guide selection of optimal recording locations and EEG features. Some brain areas may not prove to be useful: slow potential control is poor over parietal areas , and rhythms are largely limited to sensorimotor cortex. BCI systems should be flexible enough to use a variety of different EEG features as control signals. A system that can use slow potentials, or rhythms, P300 potentials, or single-unit activity alone or in combination is under development.   D.Signal Analysis and Transition Algorithms   The goal of signal analysis in a BCI system is to maximize the signal-to-noise ratio (SNR) of the EEG or single-unit features that carry the user's messages and commands. To achieve this goal, consideration of the major sources of noise is essential 44. Noise has both nonneural sources (e.g., eye movements, EMG, 60-Hz line noise) and neural sources (e.g., EEG features other than those used for communication). Noise detection and discrimination problems are greatest when the characteristics of the noisearesimilarinfrequency, time or amplitude to those of the desired signal. Signal processing methods are important in BCI design, but they cannot solve every problem. While they can enhance the signal-to-noise ratio, they cannot directly address the impact of changes in the signal itself. Factors such as motivation, intention, frustration, fatigue, and learning affect the input features that the user provides. Thus, BCI development depends on appropriate management of the adaptive interactions between system and user, as well as on selection of appropriate signal processing methods. A translation algorithm is a series of computations that transforms the BCI input features derived by the signal processing stage into actual device control commands. Stated in a different way, a translation algorithm takes abstract feature vectors that reflect specific aspects of the current state of the user's EEG or single-unit activity (i.e., aspects that encode the message that the user wants to communicate) and transforms those vectors into application-dependent device commands. Different BCI's use different translation algorithms . Each algorithm can be classified in terms of three key features: transfer function, adaptive capacity, and output. The transfer function can be linear (e.g., linear discriminant analysis, linear equations) or nonlinear (e.g., neural networks). The algorithm can be adaptive or nonadaptive. Adaptive algorithms can use simple handcrafted rules or more sophisticated machine-learning algorithms. The output of the algorithm may be discrete (e.g., letter selection) or continuous (e.g., cursor movement). The diversity in translation algorithms among research groups is due in part to diversity in their intended real-world applications. Nevertheless, in all cases the goal is to maximize performance and practicability for the chosen application.   E.BCI Applications   Brain computer interfaces have contributed in various fields of research. As briefed in  they are involved in medical, neuroergonomics and smart environment, neuromarketing and advertisement, educational and self-regulation, games and entertainment, and Security and authentication fields.   Fig 3.Classification BCI applications IV.  Applications of eeg signals in epilepsy diagnosis A.Epilepsy Epilepsy is a  disorder that causes different types of seizures. A seizure is a sudden surge in the electrical conditions in the brain . It consist of two main types. Generalized seizures which affect the whole brain. Focal, or partial seizures, which affect just one part of the brain . Impacts of seizures vary according the type which the person is affected. There are several symptoms for epilepsy fever, stroke, etc It is a common disorder which affects millions of people around the world.Types of seizures are   Focal (partial) seizures, A simple partial seizure and Complex partial seizures . Fig 4.Epilepsy Classifications B.EEG Analysis An EEG test only describes about the electrical activity of the brain at the time of the test bring conducted. If a person is affected by the seizure he/she has unusual brain activity. At other time brain activity is normal. So, if your EEG test results  are normal, it usually means that there is no epileptic activity in your brain at the time the test is being done. People affected by epilepsy have unusual electrical activity in their brain all the time, even when they are not having a seizure. From the test results a doctor can recognise the pattern of waves and he/she can diagnose it. Some people may have unusal brain patterns but they wont have epilepsy . These could be caused by other medical conditions, problems with their vision, or brain damage.So it may found that this technique may not be correct everytime.It can also show up some types of seizure. But it might not show up some focal (partial) seizures .The EEG signals only gives the brain activity and not the location of affected areas.There's a very small risk that you could have a seizure during an EEG test. This could be caused by looking at a flashing light or breathing deeply. These activities are usually part of the test. Your doctor may ask you to reduce your epilepsy medicine or have less sleep than usual before you have some types of EEG tests. This would also increase the risk that you would have a seizure around the time of having the test. If you hold a driving license, having a seizure could mean that you should not drive  until you have been seizure free for 12 months. i. Standard EEG tests: . You may be asked to breathe deeply for some minutes and also to look at a flashing light. These activities can change the electrical activity in your brain, and this will show on the computer.You will be asked to keep as still as possible during the test. Any movement can change the electrical activity in your brain, which can affect the results.Routine EEG recordings usually take 20 to 40 minutes. ii.Sleep EEG tests: EEG test is taken when you are asleep. Before the test, you may be given some medicine to make you go to sleep. The test lasts for one to two hours.It is useful when epilepsy is suspected in children under 5. This is because there are some types of epilepsy which are common in young children, where seizures mainly happen in sleep. iii.Sleep-deprived EEG tests: These  tests are done when you have had less sleep than usual. At that time, there is more chance of unusual electrical activity in the brain.It can show up subtle seizures,. Before you have a sleep-deprived EEG test, your doctor may ask you not to go to sleep at all the night before.The patient sleeping timings should be altered. You may then fall asleep while recording the activity in your brain. It extends upto few hours. iv.Ambulatory EEG tests: These tests are conducted when the patient is walking. It is designed to record the activity in your brain over a few hours, days or weeks. This means there is more chance that it will pick up unusual electrical activity in your brain, than during a standard or sleep EEG test. However, the electrodes that are attached to your head are plugged in to a small machine that records the results. You can wear the machine on a belt, so you are able to go about your daily business. You don't usually stay in hospital .The person should keep track of activities which they do. C.Diagnosis    Identifying  what kind of seizure is the patient is being affected is the most critical step in diagnosis of epilepsy. Different seizures are to be treated in the unique way ,if it is not clearly identified  false treatment may affect the patient in a greater level. The diagnosis includes meditation, medicines, etc   V. CONCLUSION   The acquisition of the EEG signals can be done effectively with the help of neurosky mindwave kit which falls under the non invasive method of EEG signal acquisition. It consists of various application rather than those mentioned above.Effective usage of those acquired EEG signals can be used in the treatment of  disesases and numerous applications like brain controlled car which is under research, home automation using brain control and brain controlled protestics etc. References 1 2 3 4 5         Vangelis P. Oikonomou_, Kostas Georgiadis_, George Liaros_, Spiros Nikolopoulos_ and Ioannis Kompatsiaris-A comparison study on EEG signal  processing techniques using EEG data 6       Preprocessing and feature extraction techniques for EEG by  Parthana Sarma1, Prakash Tripathi2, Manash Pratim Sarma3, Kandarpa Kumar Sarma4 7       Application and working of EEG by Joshna Gupta in SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE)

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