ArticleFrontiers in human neuroscience2020
Recognition of Consumer Preference by Analysis and Classification EEG Signals.
Article in Frontiers in human neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 76 citations in OpenAlex.
- Exploring neuromarketing's influence on consumer impulsivity through the lens of personality traits.Frontiers in psychology · 2026Article
- EEG feature extraction and machine learning for consumer preference analysis of whisky.Current research in food science · 2026Article
- A portable affective computing system for identifying mate preference.Scientific reports · 2024Article
- A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques.Brain informatics · 2024Review
- Machine Learning Classification of Event-Related Brain Potentials during a Visual Go/NoGo Task.Entropy (Basel, Switzerland) · 2024Article
- A comprehensive exploration of machine learning techniques for EEG-based anxiety detection.PeerJ. Computer science · 2024Article
- The effect of new e-commerce platform's OSC promotion on consumer cognition: from cognitive legitimacy and cognitive psychology perspective.Frontiers in human neuroscience · 2024Article
- Multimodal consumer choice prediction using EEG signals and eye tracking.Frontiers in computational neuroscience · 2024Article
- Explainable artificial intelligence (xAI) in neuromarketing/consumer neuroscience: an fMRI study on brand perception.Frontiers in human neuroscience · 2024Article
- Review
- Systematic Review and Future Direction of Neuro-Tourism Research.Brain sciences · 2023Review
- An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications.Sensors (Basel, Switzerland) · 2022Article
- A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research.Brain informatics · 2022Review
- BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework.Frontiers in human neuroscience · 2022Article
- Feature selection of EEG signals in neuromarketing.PeerJ. Computer science · 2022Article
- Visual preference of plant features in different living environments using eye tracking and EEG.PloS one · 2022Article
- Is Mate Preference Recognizable Based on Electroencephalogram Signals? Machine Learning Applied to Initial Romantic Attraction.Frontiers in neuroscience · 2022Article
- Like/Dislike Prediction for Sport Shoes With Electroencephalography: An Application of Neuromarketing.Frontiers in human neuroscience · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (DWT) and power spectral density (PSD), which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network (DNN) outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.