ArticleSensors (Basel, Switzerland)2024
An Innovative EEG-Based Pain Identification and Quantification: A Pilot Study.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Artificial Intelligence in Orofacial Pain: Diagnostic and Predictive Performance Across Machine Learning and Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Review
- Bridging the Gap in Pain Measurement with a Brain-Based Index.International journal of environmental research and public health · 2025Article
- Evaluating the Effects of a Progressive Kinesiotaping Treatment Protocol on Chronic Low Back Pain in Women Using Electroencephalography.Journal of functional morphology and kinesiology · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
objectiveThe present pilot study aimed to propose an innovative scale-independent measure based on electroencephalographic (EEG) signals for the identification and quantification of the magnitude of chronic pain.
methodsEEG data were collected from three groups of participants at rest: seven healthy participants with pain, 15 healthy participants submitted to thermal pain, and 66 participants living with chronic pain. Every 30 s, the pain intensity score felt by the participant was also recorded. Electrodes positioned in the contralateral motor region were of interest. After EEG preprocessing, a complex analytical signal was obtained using Hilbert transform, and the upper envelope of the EEG signal was extracted. The average coefficient of variation of the upper envelope of the signal was then calculated for the beta (13-30 Hz) band and proposed as a new EEG-based indicator, namely Piq MAIN
resultsThe main results are as follows: (1) A Piq
conclusionsThis finding indicates that Piq SIGNIFICANCE: Objective pain assessment is a valuable decision-making aid and an important contribution to pain management and monitoring.
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Registered trials
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