Neuromarketing: factual and they are totally taken in

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
electrothermal 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 stratergy.

EEG technique, on the other hand, allow neuromarketing research
to be conducted efficiently  from
anywhere. This methodology helps the researchers to measure consumer response
to an testing environment , such as a movie theatre, bar, mall.  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 state of the brain can be interpreted using the
suitable 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 is
enough to hold them to watch carefully.The EEG reading taken from that consumer
in test reveals that ,he/she will be fully attentive for the first 10 seconds
and lost their attention for next 10 seconds finally they pay attention at the
final 10 seconds.Thus improving the middle content of the video based on the
feeedback could help us to create a more creative commercial.

Neuromarketing helps firms to create more effective and creative
advertisements. This not only benefits the venture, but also the consumers who are
exposed to hundreds 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.

Brain computer interface: Introduction

                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 paralyzed 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 paralyzed patients to act on their own without depending on
anyone.

Essentials features of
BCI

BCI operation depends
on the communication which takes place between the two adaptive controllers,
the user’s brain, which produces the activity (EEG signals) measured by the BCI
system, and the system itself, which translates that activity into specific
commands to perform the tasks. Completing the BCI operation is a new skill,it
does not control our muscular organs but it controls our EEG signals as a
single unit. Each BCI uses certain algorithm to translate the
obtained input into required output control signals of our requirements. 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. Adding to its input, translation algorithm, and output,
each BCI has several other distinctive characteristics which should be
monitored. 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, appropriate user population, applications and
constraints imposed on concurrent conventional sensory input and motor Matching
BCI and the Input to user.

The input features of
proposed BCI system should be properso that it can 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. In EEG-based BCI system, effective multielectrode recording which
are  performed initially and then
periodically, can detect the changes in the user’s performance and, and can
thereby guide selection of optimal recording locations and EEG features. Some
areas of the brain may not be effectively used for the interaction because of
slow potentials and rhythms. BCI system should be designed such that it works
on the  wide variety of EEG signals.A
system works on the slow potentials,rhythms,P300 potentials, etc are under the
research.

Signal analysis and
translation algorithms:

Signal analysis is done
in the BCI system in order to enhance the signal-to-noise ratio (SNR) of the
EEG or single-unit features that carry the user’s messages and commands. To
accomplish this, consideration of the major sources of noise is essential.
Noise has two types of sources  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 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 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. From that we can state that proper interaction between the
user and the system and a effective signal processing methods helps in the BCI
development. 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 encodes 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.

BCI application:

Figure shows the
various fields in which the BCI’s are used.

 

EEG contribution to Epileptic disorder: