ArticlePloS one2026
Personalized adaptive virtual reality experience driven by electroencephalography-based pain recognition.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-pharmacological pain management represents an urgent clinical need. Emerging technologies such as virtual reality (VR) and electroencephalography (EEG)-based artificial intelligence (AI) offer promising avenues for objective pain assessment and adaptive therapeutic intervention. PURPOSE: This study aims to develop and validate a real-time, closed-loop EEG-driven VR therapy system that classifies pain levels from brain signals and delivers personalized, avatar-guided therapeutic responses.
methodsAn open-source EEG dataset (51 participants; perception condition; laser-induced pain stimuli rated 0-100) was preprocessed using bandpass filtering, Independent Component Analysis (ICA), and AutoReject. Wavelet-based features (Daubechies-4, 5 levels) were extracted from 1-second epochs and used to train two gradient-boosting classifiers: XGBoost and LightGBM. Predicted pain levels were transmitted via HTTP POST requests to Unreal Engine 5.3.2, where a MetaHuman avatar delivered adaptive therapeutic responses.
resultsLightGBM achieved 97.89% classification accuracy (cross-validation: 95.78% ± 0.82%) and XGBoost achieved 97.25% (cross-validation: 96.09% ± 0.70%) across 11 pain classes (0-10), outperforming all comparable studies in the literature. Real-time avatar responses were demonstrated across three pain categories: Slight (1-3), Moderate (4-6), and Severe (7-10).
conclusionThe study successfully demonstrates the technical feasibility of a closed-loop EEG-VR pain management system using lightweight machine learning models. The system achieves state-of-the-art pain classification accuracy with fine-grained 11-class granularity. IMPLICATIONS: This system offers a scalable, drug-free alternative for pain management applicable in clinical and rehabilitation settings. The modular design facilitates future extensions, including emotional state tracking, haptic feedback, and reinforcement learning-based personalization.
Indexed as
Identifiers
What OpenQuestion holds
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.