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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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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]
Copyright of International Journal of Neuroscience is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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PubType: Academic Journal
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  Label: Title
  Group: Ti
  Data: Preliminary findings on a deep learning model using electroencephalogram for multi-level neuropathic pain detection in post-stroke patients.
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Bobby+J%2E%2C+Sofia%22">Bobby J., Sofia</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Francis%2C+Sheeja+V%2E%22">Francis, Sheeja V.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ramya+V%2E%2C+Subha%22">Ramya V., Subha</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22C%2E+L%2E%2C+Annapoorani%22">C. L., Annapoorani</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Neuroscience%22">International Journal of Neuroscience</searchLink>. Apr2026, Vol. 136 Issue 4, p499-507. 9p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Stroke+patients%22">Stroke patients</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Brain-computer+interfaces%22">Brain-computer interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Pain+measurement%22">Pain measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Neuralgia%22">Neuralgia</searchLink><br /><searchLink fieldCode="DE" term="%22Attention%22">Attention</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Neuroscience is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=192628530
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/00207454.2025.2584081
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      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 499
    Subjects:
      – SubjectFull: Electroencephalography
        Type: general
      – SubjectFull: Stroke patients
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Brain-computer interfaces
        Type: general
      – SubjectFull: Pain measurement
        Type: general
      – SubjectFull: Neuralgia
        Type: general
      – SubjectFull: Attention
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      – TitleFull: Preliminary findings on a deep learning model using electroencephalogram for multi-level neuropathic pain detection in post-stroke patients.
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            NameFull: Francis, Sheeja V.
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            NameFull: Ramya V., Subha
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              Text: Apr2026
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              Y: 2026
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