[Submitted on 23 Sep 2026 (v1), last revised 19 Oct 2026 (this version, v5)]
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.
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)
[v5] Mon, 19 Oct 2026 10:12:20 UTC (22,120 KB)
