arXiv cs.LG + cs.RO + cs.HC· Yiqian Yang, Yiqun Duan, Chenyu Liu, Yiqi Wang, Xinliang Zhou, Chin-Teng Lin, Yu Zhang·· 3 天前AI 评分40
脑到语言解码综述：范式、信号、方法、评估与实用化
Brain-to-Language Decoding: Paradigms, Signals, Methods, Evaluation, Practical Use and Beyond
AI 导读
一篇综述系统梳理脑到语言解码，覆盖侵入式与非侵入式测量，将 Articulated、Inner、Perceived 三类任务与其涉及的神经群体、可用表征和解码输出对应起来。文章比较了已发表性能与通信成本，指出共享表征可跨记录条件和任务复用，在线沟通还依赖校准、反馈与用户控制。综述还提出从指令、语言到意义、场景与双向认知交流的五级发展路径。
正文 · 原文
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, drawing on a search without a lower year limit and source-led updates through September 2026. We connect Articulated, Inner and Perceived tasks 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
| Subjects: | Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2609.27650 [cs.CL] |
| (or arXiv:2609.27650v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.27650 arXiv-issued DOI via DataCite |
Submission history
From: Yiqian Yang [view email]
[v1]
Wed, 23 Sep 2026 10:14:47 UTC (21,336 KB)
[v2]
Thu, 24 Sep 2026 02:08:45 UTC (21,443 KB)
[v3]
Thu, 15 Oct 2026 03:34:46 UTC (21,987 KB)
[v4]
Fri, 16 Oct 2026 09:37:51 UTC (22,125 KB)
来源：arXiv cs.LG + cs.RO + cs.HC · arxiv.org