1.四川外国语大学语言智能学院,重庆 400031
2.广东外语外贸大学英语语言文化学院,广东 广州 510420
程凯文(1978- ),男,博士,四川外国语大学语言智能学院副教授,主要研究方向为认知神经科学、心理语言学、双语和语言智能。
杜圆圆(2000- ),女,四川外国语大学语言智能学院硕士生,主要研究方向为神经语言学、自然语言处理、语言智能。
赵宇浩(1999- ),男,广东外语外贸大学英语语言文化学院博士生,主要研究方向为认知语言学、语言智能、认知神经科学。
收稿:2026-02-10,
修回:2026-04-27,
录用:2026-05-18,
网络首发:2026-07-27,
纸质出版:2026-06-15
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程凯文,杜圆圆,赵宇浩.多层级语言脑解码技术的研究前瞻与发展路径[J].智能科学与技术学报,2026,08(02):183-195.
Cheng Kaiwen,Du Yuanyuan,Zhao Yuhao.Prospects and pathways for research on multi-level brain language decoding[J].Chinese Journal of Intelligent Science and Technology,2026,08(02):183-195.
程凯文,杜圆圆,赵宇浩.多层级语言脑解码技术的研究前瞻与发展路径[J].智能科学与技术学报,2026,08(02):183-195. DOI: 10.11959/j.issn.2096-6652.ZNKX26030.
Cheng Kaiwen,Du Yuanyuan,Zhao Yuhao.Prospects and pathways for research on multi-level brain language decoding[J].Chinese Journal of Intelligent Science and Technology,2026,08(02):183-195. DOI: 10.11959/j.issn.2096-6652.ZNKX26030.
语言脑解码技术为失语患者重建交流能力提供了关键路径,也是脑机接口技术发展的重要驱动力之一。近年来,基于侵入性与非侵入性脑信号的多层级语言脑解码技术取得了显著进展,但不同语言层级在脑信号表征、任务复杂度与模型方法上存在明显差异,导致相关研究在技术路径和问题侧重上比较分散,尚缺乏系统的层级化梳理。鉴于此,系统梳理了2008—2025年语言脑解码研究在音素、声调、单词、短语、句子及语篇各层级的主要进展,分析了脑信号类型、任务设定与模型架构对解码精度的影响,并就多层级解码、开放词汇解码及预训练语言模型的融合应用等问题进行了重点讨论。在此基础上,提出了未来语言脑机接口研发与智能化语言康复的若干研究路径,以期为相关交叉研究提供理论支撑与技术参考。
Brain language decoding provides a critical pathway for restoring communication in patients with aphasia and serves as an important driving force for the development of brain-computer interface (BCI) technology. In recent years
substantial progress has been made in multi-level brain language decoding based on both invasive and non-invasive brain signals. However
different linguistic levels vary markedly in brain signal representation
task complexity
and model selection
which has led to fragmented technical pathways and research emphases
while a systematic level-based synthesis is still lacking. Based on this
the major advances in brain language decoding research from 2008 to 2025 were reviewed across different linguistic levels
including phonemes
words
phrases
sentences
and discourse. The influence of brain signal modality
task setting
and model architecture on decoding accuracy was analyzed
and further key issues such as multi-level decoding
open-vocabulary decoding
and the integration of pre-trained language models were discussed. On this basis
several future research pathways were proposed for the development of language brain-computer interfaces and intelligent language rehabilitation
with the aim of providing theoretical support and technical reference for related interdisciplinary studies.
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