Applied machine learning for nociceptive pain detection using EEG spectral features
Bibliographic record
Abstract
Abstract Objective . This study explores a more reliable method for measuring nociceptive pain induced by laser stimuli from electroencephalography (EEG) signals, addressing the limitations of fixed pain scales by incorporating inter-individual variability in subjective pain tolerance. Approach . For this purpose, a public database was used that includes recordings from 51 subjects who received controlled laser stimuli at three different intensities on the back of the hand to evoke pain, while EEG activity was simultaneously recorded. Signal processing techniques were then applied to extract power in six frequency bands (e.g., alpha, beta, gamma). The extracted features were fed into machine learning algorithms to predict pain levels. This prediction was performed by comparing two data labeling strategies (reaction time versus laser intensity) and two different EEG channel configurations (62 channels versus 20 somatosensory channels). Main results . The power of EEG frequency bands, combined with machine learning, distinguished pre-stimulus from in-stimulus conditions with an average accuracy of 86%. Classification across pain levels was more challenging, reaching a maximum of 63% in the binary discrimination between high and low pain. The 62-channel configuration and the 20-channel somatosensory setup showed similar performance, although in some cases the 62-channel setup yielded better results. Incorporating temporal information from reaction times further improved performance, with time-based labels significantly outperforming intensity-based labels. Significance . Our results indicate that the best labeling system for predicting nociceptive pain levels is that one based on reaction time ( p -value < 0.001; two-sided Student’s t-test), thus suggesting that pain perception is subjective and that classifying pain solely based on stimulus intensity may not be reliable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".