综述：脑到语言解码的任务范式、信号、方法与评测
Brain-to-Language Decoding: Paradigms, Signals, Methods, Evaluation, Practical Use and Beyond
依据 arXiv 预印本摘要，Yiqian Yang 等发表综述，系统梳理脑到语言解码——把语言产生、内语与语言感知相关的神经活动转成语言或表达输出——在侵入式与非侵入式测量下的范式、信号、模型、公共资源与评测演变。
综述按言语产出、内隐言语和言语知觉三类任务梳理脑到语言解码，覆盖侵入与非侵入记录，并比较各研究在各自报告协议下的表现与通信成本。
arXiv:2609.27650v5 Announce Type: replace-cross Abstract: Brain-to-language decoding translates neural activity associated with language production, internal speech and perception into linguistic or expressive outputs. It offers a route to restoring communication after speech loss and a means of studying how the brain represents language. Advances in neural recording and representation learning have expanded the field from constrained recognition and acoustic reconstruction to text generation, streaming personalised speech and facial animation. This survey synthesises these developments across invasive and non-invasive measurements. We connect Articulated, Inner and Perceived paradigms to the neural populations they engage, the representations available to decoders and the outputs those representations can support. We examine model development, public resources and the evolution of evaluation, and compare published performance and communication costs within their reported protocols. The synthesis identifies complementary routes to progress: phonetic, acoustic and semantic targets preserve different aspects of a message; shared representations support reuse across recording conditions and tasks; and online communication increasingly depends on calibration, feedback and user control alongside decoding accuracy. Shared benchmarks enable algorithmic comparisons, while longitudinal studies reveal the demands of sustained use. We discuss these developments and their remaining limitations, then outline a prospective five-level trajectory from commands and language to meaning, scenarios and bidirectional cognitive exchange.
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来源：arXiv · Neural and Evolutionary Computing · arxiv.org