ArticleCyborg and bionic systems (Washington, D.C.)2025
Acoustic Inspired Brain-to-Sentence Decoder for Logosyllabic Language.
Article in Cyborg and bionic systems (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for brain-to-speech decoding in paralysis: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Cognitive overload in multimedia language learning: a review of attention limits and instructional design strategies.Frontiers in psychology · 2026Pooled it
- High-fidelity neural speech reconstruction through an efficient acoustic-linguistic dual-pathway framework.eLife · 2026Article
- Evaluating Japanese language learning strategies through learner and expert perspectives: a fuzzy multi-criteria decision-making approach.Frontiers in psychology · 2026Article
- Distributed cortico-subcortical networks enable robust speech state detection from sparse intracranial recordings.Frontiers in neuroscience · 2026Article
- Real-time decoding of full-spectrum Chinese using brain-computer interface.Science advances · 2025Article
- Transfer learning via distributed brain recordings enables reliable speech decoding.Nature communications · 2025Article
- Progress, challenges and future of linguistic neural decoding with deep learning.Communications biology · 2025Review
Corrections and comments
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Authors and funding
14 authors.
Funding
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Abstract
Recent advances in brain-computer interfaces (BCIs) have demonstrated the potential to decode language from brain activity into sound or text, which has predominantly focused on alphabetic languages, such as English. However, logosyllabic languages, such as Mandarin Chinese, present marked challenges for establishing decoders that cover all characters, due to its unique syllable structures, extended character sets (e.g., over 50,000 characters for Mandarin Chinese), and complex mappings between characters and syllables, thus hindering practical applications. Here, we leverage the acoustic features of Mandarin Chinese syllables, constructing prediction models for syllable components (initials, tones, and finals), and decode speech-related stereoelectroencephalography (sEEG) signals into coherent Chinese sentences. The results demonstrate a high sentence-level offline decoding performance with a median character accuracy of 71.00% over the full spectrum of characters in the best participant. We also verified that incorporating acoustic-related features into the design of prediction models substantially enhances the accuracy of initials, tones, and finals. Moreover, our findings revealed that effective speech decoding also involves subcortical structures like the thalamus in addition to traditional language-related brain regions. Overall, we established a brain-to-sentence decoder for logosyllabic languages over full character set with a large intracranial electroencephalography dataset.
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Registered trials
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