Electroencephalography (EEG) - Based Neuromarketing: Predicting Favourable and Unfavourable Consumer Reactions Using ML Techniques
Bibliographic record
Abstract
Neuromarketing is an emerging field that combines neuroscience with marketing to gain insights into unconscious consumer behaviour. Traditional methods like surveys often fail to capture real-time emotional and cognitive responses. To address this gap, this study employs EEG signal analysis to predict favourable and unfavourable consumer reactions to advertisements and products. The theoretical foundation is based on the dual-process theory, which distinguishes between fast, emotional decision-making (System 1), and slow, rational thinking (System 2). EEG markers such as alpha, beta, and theta bands are used to assess attention, engagement, and decision conflict. EEG data was collected using a single-channel Neurosky Mindwave headset from 14 participants aged 18–22. A total of 80 ads were shown, categorised by product and design type. Subject-dependent and subject-independent analyses were conducted. In the SD study, Naïve Bayes and SVM classifiers achieved a maximum accuracy of 0.62. In the SI analysis, SVM showed strong performance across product and gender-based classification. A deep learning model also produced comparable accuracy. These findings demonstrate the potential of EEG-based neuromarketing to provide deeper insights into consumer behaviour, with possible implications for both commercial and clinical applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".