RE-HPBS-IPIC: Un marco de origen cerebral del dolor de alta activación y EEG en reposo para la clasificación de la...

Accurate inter-subject pain intensity assessment using EEG remains a major challenge due to substantial inter-subject variability. This study introduces a novel framework that leverages pain-related brain dynamics and transfer learning to enable reliable inter subject pain intensity classification.

The pro-posed method first quantifies pain sensitivity from resting state EEG to identify source subjects with comparable neural pain signatures. High-activation pain brain sources are subsequently localized and remapped between source and target subjects. A classifier is trained to evaluate transfer suitability across subjects, and balanced distribution adaptation is applied to align brain source features, mitigating inter-subject variability. The adapted model infers pseudo labels for the target EEG, which guide the pain response extraction. Final classification is determined by selecting the model exhibiting the minimal cross-domain discrepancy between brain source and pain-evoked EEG features.

Experimental evaluations on real EEG datasets demonstrate that the proposed method significantly out performs three existing approaches in inter-subject pain intensity classification.

The proposed method effectively overcomes the problem of poor reliability in inter-subject pain intensity classification, providing a robust and clinically viable solution.