Rudroff Thorsten
Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland.
Brain Res. 2025 Mar 1;1850:149423. doi: 10.1016/j.brainres.2024.149423. Epub 2024 Dec 22.
This 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.
A 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.
Recent 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.
BCI-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.
本叙述性综述旨在分析脑机接口（BCI）与人工智能（AI）集成的潜在机制，评估信号采集和处理技术的最新进展，并评估人工智能增强的神经解码策略。该综述确定了关键的研究差距，并研究了脑机接口-人工智能集成多个领域中出现的解决方案。
使用包括PubMed、科学网、IEEE Xplore和Scopus（2014 - 2024年）在内的主要生物医学和科学数据库进行叙述性综述。对文献进行分析，以确定脑机接口-人工智能集成的关键进展，特别强调近期进展（2019 - 2024年）。综述过程包括对选定出版物进行主题分析，重点关注实际应用技术创新和新出现的挑战。
近期进展表明脑机接口-人工智能系统有显著改进：1）高密度电极阵列实现了高达5毫米的空间分辨率，可进行长达15个月的稳定记录；2）与传统方法相比，深度学习解码器的信息传输率提高了40%；3）自适应算法在200天的运动控制任务中保持>90%的成功率，无需重新校准；4）新型闭环优化框架将用户训练时间减少了55%，同时提高了准确性。柔性神经接口和自监督学习方法的最新进展在解决长期稳定性和跨用户泛化挑战方面显示出前景。
脑机接口-人工智能集成在提高信号质量、解码准确性和用户适应性方面取得了显著进展。虽然在长期稳定性和用户训练方面仍存在挑战，但自适应算法和反馈机制的进展表明该技术在临床应用中的可行性不断提高。电极技术、人工智能架构和闭环系统的近期创新，结合新出现的标准化框架，表明在广泛的治疗应用和人类增强应用方面正加速取得进展。
