arXiv cs.AI· Xuan Wen, Yonghao Song, Yuhong Zhang, Guoliang Li, Tzyy-Ping Jung, Xiaorong Gao·· 6 天前AI 评分22
用语言监督神经解码：LaS 让文本描述成为神经解码的通用参照
Teaching neural decoders with language
AI 导读
研究提出 Language as Supervision（LaS），通过学习到的投影将神经特征与自然语言类别描述对齐，并用候选文本原型进行分类。在视觉、运动与语音解码的七种编码器架构上，LaS 将语音解码准确率较匹配的 CLS 基线最高提升 17.15 个百分点，并支持候选集复用与标签监督的受试者、会话适配。
正文 · 原文
Abstract:Brain-computer interfaces decode neural activity, yet class-label supervision (CLS) leaves relationships among classes implicit. We propose Language as Supervision (LaS), which aligns neural features with natural-language class descriptions through learned projections and predicts classes using candidate text prototypes. We evaluate seven encoder architectures with independently trained models for visual, motor, and speech decoding from scalp and intracranial recordings. LaS improves speech-decoding accuracy by up to 17.15~pp over matched CLS baselines. Its representations support candidate-set reuse and label-supervised subject and session adaptation, while LaS enables conditional unseen-frequency decoding in steady-state visual evoked potential (SSVEP) responses. Correct class-description correspondence supports this generalization, and dual-sided attribution visualization shows neural and textual contributions to the same class-matching score. These findings suggest that text descriptions can serve as general-purpose references for learning and interpretation in neural decoding.
| Comments: | 14 pages, 8 figures |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.12921 [cs.AI] |
| (or arXiv:2610.12921v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12921 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yonghao Song [view email]
[v1]
Fri, 9 Oct 2026 03:12:42 UTC (8,275 KB)
来源：arXiv cs.AI · arxiv.org