## TL;DR
Brain-Computer Interfaces (BCIs) decode neural signals into digital commands, enabling direct brain-to-machine communication. The convergence of high-density neural implants, AI-powered decoding algorithms, and shared autonomy paradigms is transforming neurotechnology from laboratory experiments into clinical reality.

## Core Explanation
BCI pipeline: (1) Neural acquisition — electrodes record electrical activity from neurons (invasive: Utah arrays, Neuropixels, Neuralink N1 threads; non-invasive: EEG, fNIRS); (2) Signal processing — filtering, spike sorting, artifact removal; (3) Feature extraction — frequency bands, firing rates, local field potentials; (4) Decoding — machine learning translates neural patterns into commands (Kalman filters, RNNs, Transformers). The 2024 Nobel Prize in Physics recognized foundational ML contributions to neural data analysis.

## Detailed Analysis
AI advances in BCI: (1) Deep learning decoders (FBCNet, EEGNet) outperform classical methods by learning hierarchical features from raw neural data; (2) Transfer learning adapts decoders across sessions and users, reducing calibration time; (3) Shared autonomy (AI copilot) merges human intent with autonomous fine-motor control; (4) Self-supervised pretraining on large-scale neural recordings enables few-shot adaptation. China's "Brain Project" completed first quadriplegic motor function reconstruction in 2026. Key challenge: the 2-4 year lifetime of implanted electrodes due to glial scarring and immune response.

## Further Reading
- MIT Technology Review: 10 Breakthrough Technologies 2025 (BCIs)
- Neuralink PRIME Study ClinicalTrials.gov
- International BCI Society Annual Meeting