Automatic detection of nociceptive pain levels using frequency bands from electroencephalographic (EEG) signals
Bibliographic record
Abstract
Abstract Pain is considered an unpleasant but vital experience for every living being, it is extremely complex and subjective because it is composed of different variables related to their experiences. Their history, biological sex, socio/cultural context, mood, and hormonal changes can affect their perception. Nociceptive pain is more linked to tissue damage or stimulus, and this will always have a reaction that goes from its activation in the nociceptors in the peripheral nerves of the living being to the central nervous system. The most common way to assess pain today is to apply numerical scales or ”How much pain do you feel?” questionnaires, which are usually falsifiable and unreliable. Therefore, this work seeks to make use of biosignals such as Electroencephalography (EEG) to identify and evaluate pain at different levels. This nociceptive pain is generated by applying a laser on the back of the hand, which consists of three different intensities. It has been possible to differentiate between two levels of pain (high pain and low pain) with 83% accuracy using information from the power of frequency bands of the brain signal. Indicating that there are differences in the powers of the frequency bands as pain increases. Author summary Rogelio Sotero Reyes-Galaviz: Mechatronic engineer from the Polytechnic University of Tlaxcala (UPTlax), with a Master of Science degree in Biomedical Science and Technology at the Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE). He is currently pursuing a PhD in Biomedical Sciences and Technologies at INAOE. His research is focused on pain quantification using electrical brain signals (EEG) and machine learning methods. His lines of interest are Signal Processing, Electroencephalography, Stress, Music Therapy and Pain. ( rogeliosrg@inaoep.mx ) Luis Villaseñor-Pineda: Luis Villaseñor received his PhD degree in Computational Sciences from l’Université Joseph Fourier (now Université Grenoble-Alpes), France, in 1999. He is currently a senior researcher in the Computational Sciences department at the Instituto Nacional de Astrofísica, Óptica y Electŕonica, México, and a member of the Mexican Academy of Sciences, the Mexican Association of Natural Language Processing, and the Mexican System of Researchers (Level II). His research interests focus on human-computer communication using human language as well as different biosignals (speech, brain-signal, etc.). ( villasen@inaoep.mx ) Camilo E. Valderrama: Assistant Professor in the Applied Computer Science department at the University of Winnipeg, specializing in the application of machine learning, statistical models, and signal processing to extract meaningful patterns and support decision-making processes. His research spans diverse areas, including affordable fetal monitoring, reducing redundant laboratory tests in intensive care units, protecting children from unhealthy-food advertising, and validating neuromarketing principles. Prior to this, he completed a two-year postdoctoral fellowship at the University of Calgary. He holds a Ph.D. in Computer Science with a concentration in Biomedical Informatics from Emory University (Atlanta, GA, USA), a Master of Science in Informatics, and a Bachelor of Science in Software Systems Engineering from Universidad Icesi (Cali, Colombia). ( c.valderrama@uwinnipeg.ca )
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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 | 0.002 | 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".