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Preliminary findings on a deep learning model using electroencephalogram for multi-level neuropathic pain detection in post-stroke patients.

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Bibliographic Details
Title: Preliminary findings on a deep learning model using electroencephalogram for multi-level neuropathic pain detection in post-stroke patients.
Authors: Bobby J., Sofia (AUTHOR), Francis, Sheeja V. (AUTHOR), Ramya V., Subha (AUTHOR), C. L., Annapoorani (AUTHOR)
Source: International Journal of Neuroscience. Apr2026, Vol. 136 Issue 4, p499-507. 9p.
Subjects: Electroencephalography, Stroke patients, Machine learning, Deep learning, Brain-computer interfaces, Pain measurement, Neuralgia, Attention
Abstract: Aim: Neuropathic pain occurs commonly after stroke and represents a major source of disability for affected patients. This study aims to develop an accurate and computationally efficient framework for multi-level neuropathic pain detection using electroencephalography signals. Methods: A Quantum-Inspired Pyramid Depthwise Separable Residual Network is proposed, which integrates three innovations: a depthwise separable Residual Network to reduce computational complexity, a pyramid attention mechanism to capture multi-scale patterns, and a quantum-inspired transformation layer to model complex nonlinear dependencies among Electroencephalogram features. Results: Experiments conducted on benchmark electroencephalography datasets confirm that the proposed model gains a accuracy of 99.65%, with a recall of 98.00%. Conclusion: The proposed model provides a reliable solution for objective neuropathic pain detection in post-stroke patients. The framework demonstrates potential for integration into intelligent clinical decision-support and brain–computer interface-based rehabilitation systems. [ABSTRACT FROM AUTHOR]
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Database: Psychology and Behavioral Sciences Collection
Description
Abstract:Aim: Neuropathic pain occurs commonly after stroke and represents a major source of disability for affected patients. This study aims to develop an accurate and computationally efficient framework for multi-level neuropathic pain detection using electroencephalography signals. Methods: A Quantum-Inspired Pyramid Depthwise Separable Residual Network is proposed, which integrates three innovations: a depthwise separable Residual Network to reduce computational complexity, a pyramid attention mechanism to capture multi-scale patterns, and a quantum-inspired transformation layer to model complex nonlinear dependencies among Electroencephalogram features. Results: Experiments conducted on benchmark electroencephalography datasets confirm that the proposed model gains a accuracy of 99.65%, with a recall of 98.00%. Conclusion: The proposed model provides a reliable solution for objective neuropathic pain detection in post-stroke patients. The framework demonstrates potential for integration into intelligent clinical decision-support and brain–computer interface-based rehabilitation systems. [ABSTRACT FROM AUTHOR]
ISSN:00207454
DOI:10.1080/00207454.2025.2584081