Neuromarketing: useful for the companies to develop

Neuromarketing:Acquistition of EEG signals led to the new field of sciencecalled neuromarketing.It is a new way to get the feedback that you couldmeasure with a consumer device to discover which types of advertising areeffective and useful, and which types are embrassing.

Neuromarketingresearchers believe that  consumers’decisions are made in a split second,  those decisions are made subconsciously. Theystrongly believe that decision of consumers are not factual and they aretotally taken in a matter of seconds by simple attraction that the companyadvertises.The function of neuromarketing is to analyse how the customersemotions are triggered depending on the advertisement they see, how their subconcious mind react to it.

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The data it generates is extremely useful for thecompanies 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 electrothermal activity Electroencephalography (EEG)Some neuromarketing research is conducted using fMRI, whichmeasures brain activity by detecting changes in blood flow in response tostimuli. 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 stratergy.EEG technique, on the other hand, allow neuromarketing researchto be conducted efficiently  fromanywhere. This methodology helps the researchers to measure consumer responseto an testing environment , such as a movie theatre, bar, mall.  Small biosensors can be placed at distinctplaces on the head, allowing for accurate measurement of brain activity whilegiving the test subject full range of motion and ensuring their comfort.

Changes in state of the brain can be interpreted using thesuitable technique and the current status of the individual such as sleepiness,focused state ,laziness etc can be found. In a concentrated state, a 30 second commercial advertisement isenough to hold them to watch carefully.The EEG reading taken from that consumerin test reveals that ,he/she will be fully attentive for the first 10 secondsand lost their attention for next 10 seconds finally they pay attention at thefinal 10 seconds.

Thus improving the middle content of the video based on thefeeedback could help us to create a more creative commercial.Neuromarketing helps firms to create more effective and creativeadvertisements. This not only benefits the venture, but also the consumers who areexposed to hundreds of ads per day, so creating more informative, emotionally rewarding, and useful ads can enhance acustomer’s experience with a product or brand long before they consider buying.Brain computer interface: Introduction                Themajor field where the EEG signals can be effectively utilised is the BrainComputer interface (BCI).”A brain–computer interface is a communication systemthat does not depend on the brain’s normal output pathways of peripheral nervesand muscles.

” It reflects the principal reason for the interest in BCIdevelopment—the possibilities it offers for providing new augmentativecommunication technology to those who are paralyzed or have other severemovement deficits. All other augmentative communication technologies requiresome form of muscle control, and thus may not be useful for those with the mostsevere motor disabilities, such as late-stage amyotrophic lateral sclerosis,brainstem stroke, or severe cerebral palsy.Therefore by using this technique wecan make a lot paralyzed patients to act on their own without depending onanyone. Essentials features ofBCIBCI operation dependson the communication which takes place between the two adaptive controllers,the user’s brain, which produces the activity (EEG signals) measured by the BCIsystem, and the system itself, which translates that activity into specificcommands to perform the tasks. Completing the BCI operation is a new skill,itdoes not control our muscular organs but it controls our EEG signals as asingle unit.

Each BCI uses certain algorithm to translate theobtained input into required output control signals of our requirements. Thisalgorithm might include linear or nonlinear equations, a neural network, orother methods, and might incorporate continual adaptation of importantparameters to key aspects of the input provided by the user. BCI outputs can becursor 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 tooptimize communication. Adding to its input, translation algorithm, and output,each BCI has several other distinctive characteristics which should bemonitored. These include its On/Off mechanism (e.

g., EEG signals orconventional control); response time, speed and accuracy and their combinationinto information transfer rate, appropriate user population, applications andconstraints imposed on concurrent conventional sensory input and motor MatchingBCI and the Input to user.The input features ofproposed BCI system should be properso that it can be broadly applied to thecommunication needs of users with different disabilities. Most BCI systems useEEG or single-unit features that originate mainly in somatosensory or motorareas of cortex. These areas may be severely damaged in people with stroke orneurogenerative disease. Use of features from other CNS regions may provenecessary.

In EEG-based BCI system, effective multielectrode recording whichare  performed initially and thenperiodically, can detect the changes in the user’s performance and, and canthereby guide selection of optimal recording locations and EEG features. Someareas of the brain may not be effectively used for the interaction because ofslow potentials and rhythms. BCI system should be designed such that it works onthe  wide variety of EEG signals.

A systemworks on the slow potentials,rhythms,P300 potentials, etc are under theresearch.Signal analysis andtranslation algorithms:Signal analysis is donein the BCI system in order to enhance the signal-to-noise ratio (SNR) of theEEG or single-unit features that carry the user’s messages and commands. Toaccomplish this, consideration of the major sources of noise is essential.Noise has two types of sources  bothnonneural sources (e.

g., eye movements, EMG, 60-Hz line noise) and neuralsources (e.g., EEG features other than those used for communication). Noisedetection and eliminating those noises will be very difficult if the frequency,amplitude and other parameters of noises are similar to the required system.

.While they can enhance the signal-to-noise ratio, they cannot directly addressthe impact of changes in the signal itself. Factors such as motivation,intention, frustration, fatigue, and learning affect the input features thatthe user provides.

From that we can state that proper interaction between theuser and the system and a effective signal processing methods helps in the BCIdevelopment. A translation algorithm is a series of computationsthat transforms the BCI input features derived by the signal processing stageinto actual device control commands. Stated in a different way, a translationalgorithm takes abstract feature vectors  that encodes the message that the user wantsto communicate and transforms those vectors into application-dependent devicecommands.

Different BCI’s use different translation algorithms . Each algorithmcan be classified in terms of three key features: transfer function, adaptivecapacity, and output. The transfer function can be linear (e.g., lineardiscriminant analysis, linear equations) or nonlinear (e.g.

, neural networks).The algorithm can be adaptive or nonadaptive. Adaptive algorithms can usesimple handcrafted rules or more sophisticated machine-learning algorithms. Theoutput of the algorithm may be discrete (e.g., letter selection) or continuous(e.

g., cursor movement). The diversity in translation algorithms among researchgroups is due in part to diversity in their intended real-world applications.Nevertheless, in all cases the goal is to maximize performance andpracticability for the chosen application.

BCI application:Figure shows thevarious fields in which the BCI’s are used. EEG contribution to Epileptic disorder:

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