Liking Status Estimation Using EEG Signals and Mode Decomposition Method
Bibliographic record
Abstract
Emotion estimation is a very important and popular research area from human-computer interaction point of view. In this study, a liking estimation method is proposed by using electroencephalogram (EEG) signals for neuromarketing applications. The liking status is estimated by using signal processing and machine learning methods applied to EEG recordings taken while the subjects watched the advertisement videos of two different car brands. After viewing videos, the participants were asked to give ratings from 1 to 5 while they are presented the picture of 7 car sections (front view, console, side view, rear view, stop lamp, logo and front grille) taken from the advertisement video. EEG segments corresponding to these regions were analyzed by Multivariate Empirical Mode Decomposition (MEMD) method. Various features were calculated from the extracted intrinsic mode functions (IMF) and liking status classification was performed. The successful results show that the proposed MEMD-based method may be used in neuromarketing studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".