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Artificial intelligence for brain-to-speech decoding in paralysis: a systematic review.

Sanae Belfrouh1, Fatima Zahra Salmam1, Rahhal Errattahi1

  • 1Laboratory of Information Technologies, National School of Applied Sciences, University of Chouaib Doukkali, El Jadida, 24000, Morocco.

BMC Medical Informatics and Decision Making
|May 26, 2026
PubMed
Summary

Artificial intelligence (AI) and brain-computer interfaces (BCIs) show promise for restoring communication in paralysis. Invasive BCIs offer higher accuracy, but validation in paralyzed individuals remains a critical gap for both invasive and non-invasive methods.

Keywords:
Artificial intelligenceBrain-computer interfaceNeural decodingParalysis rehabilitationSpeech neuroprosthesisSystematic review

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Area of Science:

  • Neuroscience and Artificial Intelligence
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Communication loss is a significant challenge for individuals with paralysis.
  • Brain-computer interfaces (BCIs) combined with artificial intelligence (AI) offer a potential solution for restoring speech.
  • Decoding speech from brain signals is an active area of research with various approaches.

Purpose of the Study:

  • To systematically review the use of AI in decoding speech from brain signals using both invasive and non-invasive neural interfaces.
  • To evaluate the performance, methodologies, and quality of existing studies in this field.
  • To identify key challenges and propose a framework for future research and clinical application.

Main Methods:

  • Systematic literature review following PRISMA guidelines, analyzing 115 studies (2019-2025).
  • Data extraction on acquisition protocols, signal preprocessing, and AI architectures.
  • Quality assessment using QUADAS-2, with a focus on bias and validation in paralyzed populations.

Main Results:

  • Invasive BCIs showed higher median classification accuracy (77.7%) than non-invasive methods (73.0%), though task complexity and evaluation paradigms differ.
  • Hybrid CNN/RNN architectures and transformers outperformed traditional AI models.
  • Significant limitations were found in study quality, with high risk of selection bias (62.6%) and limited validation in paralyzed individuals (5.2%).
  • No non-invasive study demonstrated functional speech decoding in paralyzed populations, highlighting a critical translational gap.

Conclusions:

  • While AI-powered BCIs show feasibility for speech decoding, particularly invasive methods, substantial validation and quality improvements are needed.
  • The lack of non-invasive functional speech decoding in paralyzed individuals is a priority for future research.
  • A decision framework is proposed to guide future development considering accuracy, cost, and clinical applicability.