Decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution

Area of Science:

• Neuroscience
• Biomedical Engineering
• Computer Science

Background:

• Brain-Computer Interface (BCI) systems translate neural activity into commands.
• Artificial Intelligence (AI) enhances BCI capabilities through advanced signal processing and decoding.
• Integration of BCI and AI is crucial for developing sophisticated neuroprosthetics and assistive technologies.

Purpose of the Study:

• To analyze the mechanisms of Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration.
• To evaluate recent advances in neural signal acquisition, processing, and AI-enhanced decoding.
• To identify research gaps and explore emerging solutions in BCI-AI domains.

Main Methods:

• A narrative review of literature from 2014-2024 was conducted.
• Major scientific databases (PubMed, Web of Science, IEEE Xplore, Scopus) were utilized.
• Thematic analysis focused on recent developments (2019-2024), practical applications, and challenges in BCI-AI integration.

Main Results:

• High-density electrode arrays offer improved spatial resolution and long-term stability.
• Deep learning decoders significantly enhance information transfer rates (40% improvement).
• Adaptive algorithms and closed-loop systems improve accuracy and reduce user training time (55% reduction).

Conclusions:

• BCI-AI integration demonstrates substantial progress in signal quality, decoding accuracy, and user adaptation.
• Advances in adaptive algorithms and neural interfaces highlight growing clinical viability.
• Innovations in electrode technology, AI, and closed-loop systems accelerate progress toward therapeutic and augmentation applications.