Key result
Neurophysiological sensors like EEG and fNIRS achieved high classification accuracy for specific pain stimuli, though current datasets lack integration of neurological and peripheral signals.
Why the study?
Current objective pain measures relying on pain intensity and correlating self-reports with physiological signals often yield inconsistent results due to individual variability.
Do multimodal digital neurophysiological sensors improve objective pain assessment compared to self-report in patients with pain?
Systematic Review
Do multimodal digital neurophysiological sensors improve objective pain assessment compared to self-report in patients with pain?
Multimodal neurophysiological sensors like EEG and fNIRS show promise for objective pain assessment, but current datasets lack the necessary integration and multidimensional labeling to advance personalized pain management.
May advance objective pain assessment beyond self-report; extends accuracy findings but leaves open multimodal dataset integration.
People have been looking for an objective measure of chronic pain for decades. Current objective measures can be achieved by correlating patients’ self-report with physiological signal features, but such measures often yield inconsistent results due to individual variability. While existing approaches heavily rely on pain intensity, this study reviewed literatures in differentiating pain types and pain locations using neurophysiological sensors. Results showed that using modalities like EEG and fNIRS, high classification accuracy for specific stimuli can be achieved. However, most existing datasets lack integration of both neurological and peripheral physiological signals and do not label multimodal pain dimensions, such as type, intensity, and location. The review highlights an urgent need for datasets that combine these modalities to support objective pain assessment. Future research should also prioritize multidimensional labeling, population diversity, and validation frameworks to advance personalized pain management and rehabilitation workflows.
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Zhu et al. (2025) conducted a systematic review in Chronic pain. Multimodal digital neurophysiological sensors (e.g., EEG and fNIRS) was evaluated on Differentiation of pain types and pain locations. Neurophysiological sensors like EEG and fNIRS achieved high classification accuracy for specific pain stimuli, though current datasets lack integration of neurological and peripheral signals.
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