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Updated: Mar 20, 2026

Assessment and Communication for People with Disorders of Consciousness
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Improving consciousness assessment through neuroadaptive artificial intelligence and quantum-enhanced brain-computer

Olivier Oullier1, Florian Roser2, Paul Barbaste3

  • 1Human-Computer Interaction Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates; Inclusive Brains, Marseille, France; Institute for Artificial Intelligence, Biotech Dental Group, Salon de Provence, France.

Clinical Neurology and Neurosurgery
|March 18, 2026
PubMed
Summary

Neuroadaptive artificial intelligence (NA-AI) and quantum computing could enhance brain-computer interfaces (BCIs) for assessing consciousness in patients with disorders of consciousness (DoC). This technology promises more accurate, personalized, and reliable diagnostics for non-communicative individuals.

Keywords:
AI world modelBrain-computer interfacesCognitive motor dissociationNeuroadaptive AIQuantum machine learning

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Quantum Computing

Background:

  • Assessing consciousness in patients with disorders of consciousness (DoC) is challenging, especially with motor impairments.
  • Current brain-computer interfaces (BCIs) are often experimental and lack personalization.

Purpose of the Study:

  • To explore how neuroadaptive artificial intelligence (NA-AI) and quantum-enhanced computation can improve BCIs for consciousness assessment.
  • To enhance the accuracy, speed, and reliability of diagnosing disorders of consciousness.

Main Methods:

  • Utilizing generative and self-adapting AI models for personalized BCI calibration.
  • Employing AI world-model approaches for dynamic neurophysiological representations.
  • Investigating quantum-enhanced machine and deep learning to overcome computational bottlenecks in BCIs.

Main Results:

  • NA-AI enables real-time adaptation to individual neurophysiological signals, improving BCI robustness and decoding.
  • AI world models allow interpretation of neural activity within evolving brain states.
  • Quantum-enhanced AI may alleviate computational challenges in high-dimensional optimization and pattern discovery for BCIs.

Conclusions:

  • The convergence of NA-AI and quantum computation holds potential for sensitive, rapid, and reliable consciousness assessment in DoC.
  • Validated quantum-AI BCIs could reduce diagnostic uncertainty, improve prognostication, and aid ethical decision-making for non-communicative patients.
  • Rigorous clinical validation and governance are essential for safe and effective deployment of these advanced BCI technologies.