PEEG-HAR: A novel pain-evoked EEG extraction method guided by the adaptive localization of high-activation-rate pain-related EEG sources

Laser-evoked potentials (LEPs) are widely recognized as optimal for pain assessment, but raw EEG signals are often contaminated by noise and background activity, making LEPs extraction challenging. Existing wavelet-based methods construct templates using all trials. However, since LEPs exhibit variations across different pain intensities, the use of all trials may result in the loss of discriminative features essential for distinguishing between varying levels of pain intensity. In this study, EEG source signals are obtained using electrophysiological source imaging, and high-activation-rate pain-related EEG sources are identified based on their activation characteristics. A within-subject pain intensity model based on a support vector machine is then developed to link recorded EEG signals with historical EEG samples. The model guides the selection of suitable EEG segments to construct a pain-specific template, which is subsequently used to reconstruct the recorded EEG signals by exploiting the time–frequency distribution of pain-related wavelet coefficients, thereby facilitating more effective extraction of LEPs. Experiments on real EEG recordings confirm that the proposed method can extract signals that more closely reflect genuine pain-evoked EEG activity, thereby enhancing the representation of pain and significantly improving subsequent classification performance, with binary accuracy increasing from 59.61% to 81.13% and three-class accuracy from 39.22% to 64.23%. The method addresses the challenge of insufficient pain expression in raw signals and provides a data foundation for developing objective pain biomarkers.