A2 Refereed review article in a scientific journal
Decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution
Authors: Rudroff, Thorsten
Publisher: Elsevier
Publication year: 2025
Journal: Brain Research
Journal name in source: Brain Research
Article number: 149423
Volume: 1850
ISSN: 0006-8993
eISSN: 1872-6240
DOI: https://doi.org/10.1016/j.brainres.2024.149423
Web address : https://doi.org/10.1016/j.brainres.2024.149423
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/484692652
Abstract
ObjectivesThis narrative review aims to analyze mechanisms underlying Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration, evaluate recent advances in signal acquisition and processing techniques, and assess AI-enhanced neural decoding strategies. The review identifies critical research gaps and examines emerging solutions across multiple domains of BCI-AI integration.MethodsA narrative review was conducted using major biomedical and scientific databases including PubMed, Web of Science, IEEE Xplore, and Scopus (2014–2024). Literature was analyzed to identify key developments in BCI-AI integration, with particular emphasis on recent advances (2019–2024). The review process involved thematic analysis of selected publications focusing on practical applications, technical innovations, and emerging challenges.ResultsRecent advances demonstrate significant improvements in BCI-AI systems: 1) High-density electrode arrays achieve spatial resolution up to 5 mm, with stable recordings over 15 months; 2) Deep learning decoders show 40 % improvement in information transfer rates compared to traditional methods; 3) Adaptive algorithms maintain >90 % success rates in motor control tasks over 200-day periods without recalibration; 4) Novel closed-loop optimization frameworks reduce user training time by 55 % while improving accuracy. Latest developments in flexible neural interfaces and self-supervised learning approaches show promise in addressing long-term stability and cross-user generalization challenges.ConclusionsBCI-AI integration shows remarkable progress in improving signal quality, decoding accuracy, and user adaptation. While challenges remain in long-term stability and user training, advances in adaptive algorithms and feedback mechanisms demonstrate the technology’s growing viability for clinical applications. Recent innovations in electrode technology, AI architectures, and closed-loop systems, combined with emerging standardization frameworks, suggest accelerating progress toward widespread therapeutic use and human augmentation applications.
ObjectivesThis narrative review aims to analyze mechanisms underlying Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration, evaluate recent advances in signal acquisition and processing techniques, and assess AI-enhanced neural decoding strategies. The review identifies critical research gaps and examines emerging solutions across multiple domains of BCI-AI integration.MethodsA narrative review was conducted using major biomedical and scientific databases including PubMed, Web of Science, IEEE Xplore, and Scopus (2014–2024). Literature was analyzed to identify key developments in BCI-AI integration, with particular emphasis on recent advances (2019–2024). The review process involved thematic analysis of selected publications focusing on practical applications, technical innovations, and emerging challenges.ResultsRecent advances demonstrate significant improvements in BCI-AI systems: 1) High-density electrode arrays achieve spatial resolution up to 5 mm, with stable recordings over 15 months; 2) Deep learning decoders show 40 % improvement in information transfer rates compared to traditional methods; 3) Adaptive algorithms maintain >90 % success rates in motor control tasks over 200-day periods without recalibration; 4) Novel closed-loop optimization frameworks reduce user training time by 55 % while improving accuracy. Latest developments in flexible neural interfaces and self-supervised learning approaches show promise in addressing long-term stability and cross-user generalization challenges.ConclusionsBCI-AI integration shows remarkable progress in improving signal quality, decoding accuracy, and user adaptation. While challenges remain in long-term stability and user training, advances in adaptive algorithms and feedback mechanisms demonstrate the technology’s growing viability for clinical applications. Recent innovations in electrode technology, AI architectures, and closed-loop systems, combined with emerging standardization frameworks, suggest accelerating progress toward widespread therapeutic use and human augmentation applications.
Downloadable publication This is an electronic reprint of the original article. |  |
UTU Research Portal