BCI-based Consumers’ Preference Prediction using Single Channel Commercial EEG Device
- Resource Type
- Conference
- Authors
- Ishtiaque, Farhan; Mashrur, Fazla Rabbi; Touhidul Islam Miya, Mohammad; Rahman, Khandoker Mahmudur; Vaidyanathan, Ravi; Anwar, Syed Ferhat; Sarker, Farhana; Mamun, Khondaker A.
- Source
- 2022 25th International Conference on Computer and Information Technology (ICCIT) Computer and Information Technology (ICCIT), 2022 25th International Conference on. :43-48 Dec, 2022
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Support vector machines
Performance evaluation
Computational modeling
Neuromarketing
Feature extraction
Brain modeling
Electroencephalography
Brain Computer Interface
EEG
Signal Processing
Machine Learning
- Language
Brain-Computer Interface (BCI) technology is used in neuromarketing to learn how consumers respond to marketing stimuli. This helps evaluate the marketing stimuli which is traditionally done using marketing research procedures. BCI-based neuromarketing promises to replace these traditional marketing research procedures which are time-consuming and costly. Although BCI-based neuromarketing has its difficulty as EEG devices are inconvenient for consumer-grade applications. This study is performed to predict consumers’ affective attitude (AA) and purchase intention (PI) toward a product using EEG signals. EEG signals are collected using a single channel consumer-grade EEG device from 4 healthy participants while they are subject to 3 different types of marketing stimuli; product, promotion, and endorsement. Multi-domain features are extracted from the EEG signals after pre-processing. 52 features are selected among those using SVM-based Recursive Feature Elimination. SMOTE algorithm is used to balance out the dataset. Support Vector Machine (SVM) is used to classify positive and negative affective attitude and purchase intention. The model manages to achieve an accuracy of 88.2% for affective attitude and 80.4% for purchase intention proving the viability of consumer-grade BCI devices in neuromarketing.