EEG Based Preference Classification For Neuromarketing

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Presented by:
Faiyaz Ahmed & Injamamul Haque Sourov
Department of Electrical & Electronic Engineering
INDEPENDENT UNIVERSITY, BANGLADESH
EEE 400 PROGRESS REPORT PRESENTATION (1ST TERM)
EEG Based Preference Classification For
Neuromarketing
Supervisor: Prof. Md. Kafiul Islam

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 Background, Motivation and Objectives
 Existing systems and related works (literature review)
 Problems and challenges in the existing systems
 Possible solutions and proposed system
 Research Methodology & Proposed System
 Work Plan
 Expected outcome of the project
 Impact of project on the
 societal, health, safety, legal and cultural issues
 environment and sustainability
 Proposed budget for implementing the project
 Summary
 References
Outline

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What is Neuromarketing?
 Modern marketing research
technique
 Uses neuroscientific techniques to
analyze consumer behavior
Why Electroencephalogram (EEG)?
 Electrical signals from brain collected
via electrodes place over scalp
 Relatively cheap, portable and non-
invasive technique
Background and Motivation
Image source: [1]

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 Xx
 Xx
 Xx
 Xx
 Xx
 Xx
 xx
Background and Motivation (contd..)

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Motivation:
 Create efficient model for
marketers
 Remove dependency on
consumers to accurate
report emotions
 Insufficient research
content available
 Utilizing dataset created
at IUB
Background and Motivation (contd..)
Figure: Leading clients of NeuroFocus [2]

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Goal of the project:
 Build predictive models to classify user’s preference based on EEG data
The objectives of the proposed system are:
 Create predictive models for each gender (refers to dataset)
 Determine classifier accuracy for different bands
 Suggest the most relevant band for predicting user preference
 Determine the best algorithm for predicting user preference
Objectives

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Research Methodology
Figure: Research methodology to be followed

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Proposed System

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Work Plan
Period Research Work Comments
1st trimester
o Study of BCI applications and its subfields
o Comparative study of different biomedical signals
o Tutorials on EEG signal processing methodology
o Selection of database on Neuromarketing
Chose Neuromarketing,
subfield, reviewed
literature, and dataset
obtained from AGenCy lab
at IUB.
2nd trimester
o Label selected dataset
o Apply signal processing & artifact removal techniques
o Study feature extraction and selection techniques
o Study of different classifiers for trials
Perform signal
preprocessing and select
features for classification
3rd trimester
o Apply different classifiers as trials
o Performance evaluation of classifier outputs
o Repeat until satisfactory results obtained
Try different classifiers
until expected accuracy is
obtained

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 Produce high mean classifier accuracy (80% or more) for the combined
band features for each gender and hence establish predicting models
that work for both genders.
Expected outcome of the project

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Societal:
 Influence consumers more
effectively
Consumers:
 Might feel as an invasion of
private thought
 Fear of subconscious
influence
 Fear of being marked by
companies
Impact of project on societal, health & legal issues
Image source: [3]

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Impact of project on environment & sustainability
Environment:
 Reduce advertisement and
wasteful production cost
 Generate effective
environmental advertisements
Sustainability:
 Headsets can cause user
discomfort
Image source: [4]

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Proposed Budget
Sl Item Justification Quantity
Unit Price
(BDT)
Price
(BDT)
1.
2.
3.
4.
5.
Total (BDT)
In words:
example

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 xx
Summary

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1. Image source: https://www.singlegrain.com/digital-marketing/neuromarketing-101-how-neuroscience-affects-
customers-buying-behaviors/
2. Image source: https://smartboost.com/blog/what-is-neuromarketing/
3. Image source: https://www.dhakatribune.com/opinion/2018/11/12/neuromarketing-reads-you-to-provide-solutions
4. Image souroce: https://www.mauriziopittau.it/digital-marketing/neuromarketing-for-b2b
5. Md. Mahamudul Hasan, Arif Hossain, Noorjahan Akter Akhi and M. A. Razzak, “Design and Implementation of a Low
Cost Power Diagnosis Node for Monitoring the Standalone PV System in the Mockery of IoT”, Accepted for oral
presentation at the International Conference on Automation, Signal Processing and Control (iCASIC), 27-28 February,
2020, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Selected References

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Any questions, comments or suggestions?