ReviewBrain informatics2020
Technological advancements and opportunities in Neuromarketing: a systematic review.
Review in Brain informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neuromarketing and the gaming consumer: a systematic review of attention, emotion, and memory in player engagement and retention.Frontiers in neuroergonomics · 2026Pooled it
- A gender-aware saliency prediction system for web interfaces using deep learning and eye-tracking data.Brain informatics · 2025Article
- A hybrid neuromarketing approach exploiting EEG graph signal processing and gaze dynamic patterning.Brain informatics · 2025Article
- The Neuroscience of Marketing: Noninvasive Neuromarketing Methods for Understanding Consumer Choice and Priorities.F1000Research · 2025Review
- Editorial: Machine-learning/deep-learning methods in neuromarketing and consumer neuroscience.Frontiers in human neuroscience · 2025Article
- Machine learning-based viewers' preference prediction on social awareness advertisements using EEG.Frontiers in human neuroscience · 2025Article
- A Bibliometric Analysis of the Trends and Impact of Neuromarketing Research: Peering Into the Consumer Brain.Cureus · 2024Review
- A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques.Brain informatics · 2024Review
- I DARE: IULM Dataset of Affective Responses.Frontiers in human neuroscience · 2024Article
- Functional Near-Infrared Spectroscopy as a Personalized Digital Healthcare Tool for Brain Monitoring.Journal of clinical neurology (Seoul, Korea) · 2023Review
- Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review.Frontiers in human neuroscience · 2023Review
- Consumer Behaviour to Be Considered in Advertising: A Systematic Analysis and Future Agenda.Behavioral sciences (Basel, Switzerland) · 2022Review
- A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research.Brain informatics · 2022Review
- Article
- An EEG-Based Neuromarketing Approach for Analyzing the Preference of an Electric Car.Computational intelligence and neuroscience · 2022Article
- Bibliometric-Based Evaluation of the Neuromarketing Research Trend: 2010-2021.Frontiers in psychology · 2022Article
- BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework.Frontiers in human neuroscience · 2022Article
- Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces.APL bioengineering · 2021Review
- Near-channel classifier: symbiotic communication and classification in high-dimensional space.Brain informatics · 2021Article
- Threats to Global Mental Health From Unregulated Digital Phenotyping and Neuromarketing: Recommendations for COVID-19 Era and Beyond.Frontiers in psychiatry · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.
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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.