EEG-based Pain Level Classification Using Time-Frequency Features and Deep Learning
Bibliographic record
Abstract
Nociception refers to the sensory mechanism activated by external stimuli that elicit physiological responses interpreted as pain. The perception of pain is inherently subjective, influenced by individual emotions and behaviors. Although analog pain scales have been created to address this variability, researchers are now pursuing more objective assessment methods. Recent technological advancements are exploring biomarkers to render pain assessment more quantitative and less reliant on subjective interpretation by using biosignals as the primary data source. This study presents a method for discriminating between low and high-pain classes using a public database of biosignals collected from multiple subjects exposed to a transcutaneous laser. The method leverages time-frequency information by applying a wavelet transform to EEG signals and training an inter-subject classifier. This classifier, based on a Transformer architecture, is evaluated using a leave-one-out cross-validation scheme. The experiments demonstrate that it is possible to distinguish between high and low pain states. Given the dataset information, two experiments were conducted considering two ways of labeling the data: based on laser intensity or reaction time. The best results were obtained with the second approach, where 16 out of 51 subjects achieved classification accuracies above 70%. In contrast, with the first scheme, only 4 subjects reached a similar percentage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".