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AI-Driven Multimodal Brain-State Decoding for Personalized Closed-Loop TENS: A Comprehensive Review.

Jiahao Du1, Shengli Luo1, Ping Shi1

  • 1Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China.

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|September 27, 2025
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Summary
This summary is machine-generated.

Transcutaneous Electrical Nerve Stimulation (TENS) can become a personalized therapy using brain-state decoding. This adaptive approach moves beyond static treatments for dynamic chronic pain conditions.

Keywords:
Brain-State DecodingClosed-Loop NeuromodulationMultimodal NeuroimagingPersonalized NeuromodulationTranscutaneous Electrical Nerve Stimulation (TENS)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Chronic pain is a complex brain-wide condition inadequately managed by current static treatments.
  • Transcutaneous Electrical Nerve Stimulation (TENS) is a traditional modality needing advancement.
  • Precision neuromodulation requires personalized, adaptive approaches.

Purpose of the Study:

  • To outline a framework for transforming TENS into an intelligent, adaptive therapeutic platform.
  • To explore the integration of neuroimaging and machine learning for personalized TENS.
  • To shift from fixed TENS protocols to closed-loop, responsive neuromodulation.

Main Methods:

  • Integrating multimodal neuroimaging (fMRI, EEG, fNIRS) with real-time machine learning.
  • Utilizing brain-state decoding and adaptive algorithms for TENS.
  • Developing a translational framework for reengineering TENS technology.

Main Results:

  • Envisioning a paradigm shift in TENS from open-loop to closed-loop modulation.
  • Highlighting the potential for AI-driven infrastructures in neuromodulation.
  • Identifying key neurophysiological mechanisms and ethical considerations.

Conclusions:

  • TENS can evolve into a precision neuromodulation system tailored to individual chronic pain profiles.
  • This adaptive approach promises more effective management of chronic pain.
  • The framework supports broader neuroadaptive healthcare applications beyond pain management.