EEG Classification to Food Stimuli in Diverse Weight Groups with Regression Analysis of Eating Behavior Questionnaires
Bibliographic record
Abstract
Obesity and overweight are well-documented risk factors for numerous diseases that negatively impact life expectancy and quality of life, including cardiovascular diseases, diabetes, and cancer.Although the effects of weight status on brain function have been extensively studied, the application of machine learning (ML) and deep learning (DL) techniques in this domain remains underexplored.This study aims to address this gap by creating a unique dataset comprising electroencephalography (EEG) data from 19 channels, recorded while participants with varying body mass indices were exposed to visual food cues.The primary objective was to classify the differences in brain signals between normalweight and overweight/obese individuals using advanced DL methods.To mitigate overfitting and data imbalance, tabular data augmentation was employed.Additionally, the Supervised Tabular Meta-Learning (SuperTML) method was utilized to embed EEG features into images, marking a novel application for this type of data.Classification results indicate that DenseNet-121 achieved the highest accuracy, with a rate of 0.97 at channel T4.Regionally, the temporal area yielded the best average accuracy rates.Furthermore, the study investigated the correlation between EEG data and eating behavior through regression analysis, applying Random Forest, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Voting ensemble regression models to the participants' questionnaire responses.A significant relationship between EEG data and the questionnaires was identified, with the LightGBM regressor achieving an R² value of 0.966.These findings demonstrate superior performance compared to existing literature in several aspects.This study underscores the potential of DL in enhancing our understanding of the neural mechanisms underlying eating behaviors in individuals with different body weights and provides a robust methodological framework for future research in this field.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".