ArticleNeuroImage2020
Pain phenotypes classified by machine learning using electroencephalography features.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed, 63 citations in OpenAlex.
- Transient frontal spectral events from EEG predict antidepressant response to sertraline in depression.Journal of psychiatric research · 2026Trial
- Electroencephalography research in neuropathic pain (Review).Molecular medicine reports · 2026Review
- Artificial Intelligence in the Assessment of Males with Chronic Pelvic Pain Syndrome: An Up-to-Date UPOINTS-Based Narrative Mapping Review.Diagnostics (Basel, Switzerland) · 2026Review
- EEG-based clustering shows distinct separation of chronic pain patients before spinal cord stimulation surgery.NeuroImage · 2026Article
- Predicting Pain: Electroencephalography Signatures of Neural Integration During Experimental Tonic Thermal Pain.European journal of pain (London, England) · 2026Article
- Dysfunctional oscillatory bursting patterns linked to working memory in adolescents with attention deficit hyperactivity disorder.Cognitive, affective & behavioral neuroscience · 2026Article
- EEG neurofeedback for the treatment of neuropathic pain in the elderly-a mechanistic review.GeroScience · 2026Review
- Breathing-Controlled Electrical Stimulation (BreEStim) Selectively Modulates Affective and Cognitive Components of Pain-An EEG Study.Bioengineering (Basel, Switzerland) · 2026Article
- The Association between Oscillatory Burst Features and Human Working Memory Accuracy.Journal of cognitive neuroscience · 2026Article
- [Application of support vector machine in identifying neuromodulation states in patients with zoster-associated pain].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- Transient frontal spectral events from EEG predict antidepressant response to sertraline in depression.medRxiv : the preprint server for health sciences · 2026Article
- AI applications across the cancer pain care Continuum: applications and future directions.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Artificial intelligence-enhanced pain management: the NXTSTIM EcoAI platform.Pain management · 2025Review
- Machine learning classification of active viewing of pain and non-pain images using EEG does not exceed chance in external validation samples.Cognitive, affective & behavioral neuroscience · 2025Article
- Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain.Scientific reports · 2025Article
- Generalisation of EEG-Based Pain Biomarker Classification for Predicting Central Neuropathic Pain in Subacute Spinal Cord Injury.Biomedicines · 2025Article
- The intrinsic reason why complementary tests (clinical neurophysiology, neuroimaging, skin biopsy) cannot establish the diagnosis of neuropathic pain.Frontiers in pain research (Lausanne, Switzerland) · 2025Article
- Electroencephalographic signatures of migraine in small prospective and large retrospective cohorts.Scientific reports · 2024Article
- Research trends in exercise therapy for the treatment of pain in postmenopausal osteoporosis over the past decade: A bibliometric analysis.World journal of orthopedics · 2024Article
- Could biomarkers mean better pain treatment?Nature · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 4 institutions in 2 countries.
Funding
Abstract
Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.