Millions of individuals worldwide suffer from sensory and communication deficits caused by neurodegenerative diseases, stroke, or trauma. Brain-computer interfaces (BCIs) offer a promising avenue for sensory and motor restoration. However, the scientific literature remains highly fragmented between invasive neuroprosthetics and non-invasive electrophysiological decoders, with a lack of consistent terminology and comparison metrics. This chapter proposes a unified 2 x 2 framework categorizing BCIs along two axes: degree of invasiveness (invasive vs. non-invasive) and signal direction (afferent sensory-IN vs. efferent sensory-OUT). We define and distinguish the paradigms of restoration, substitution, and augmentation. Furthermore, we outline a structural roadmap for the convergence of these modalities over near-, medium-, and long-term horizons, focusing on physical limits and the integrative role of machine learning foundation models.

翻译：全球有数百万人因神经退行性疾病、中风或创伤而遭受感觉和沟通障碍。脑机接口（BCI）为感觉和运动恢复提供了一条有前景的途径。然而，相关科学文献在侵入式神经假体与非侵入式电生理解码器之间仍然高度分化，缺乏一致的术语和比较指标。本章提出了一个统一的 2 x 2 框架，沿两个轴对 BCI 进行分类：侵入程度（侵入式 vs. 非侵入式）和信号方向（传入感觉输入 vs. 传出感觉输出）。我们定义并区分了恢复、替代和增强三种范式。此外，我们概述了一个结构化的路线图，用于在近期、中期和远期实现这些模态的融合，重点关注物理极限以及机器学习基础模型的整合作用。