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Real-time decoding of full-spectrum Chinese using brain-computer interface.

Youkun Qian1,2,3, Changjiang Liu4, Peixi Yu5,6

  • 1Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China.

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|November 5, 2025
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Summary
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This study presents a real-time brain-computer interface (BCI) for decoding Mandarin Chinese speech directly from neural signals. The system achieved 71.2% accuracy in identifying syllables, enabling sentence communication for individuals with speech impairments.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Linguistics

Background:

  • Speech brain-computer interfaces (BCIs) are crucial for restoring communication in patients with anarthria due to conditions like ALS or stroke.
  • Existing speech decoding research primarily focuses on English, facing challenges with tonal, monosyllabic languages like Mandarin Chinese.

Purpose of the Study:

  • To develop and evaluate a real-time Mandarin Chinese speech BCI capable of decoding monosyllabic units directly from neural signals.
  • To address the limitations of current phoneme-driven approaches for tonal languages.

Main Methods:

  • Utilized a 256-channel microelectrocorticographic (ECoG) BCI system.
  • Developed a direct syllable neural decoding architecture for Mandarin Chinese.
  • Tested the system in a single-character reading task.

Main Results:

  • Achieved robust decoding of 394 distinct Mandarin syllables directly from neural signals.
  • Demonstrated a median syllable identification accuracy of 71.2% in a single-character reading task.
  • Successfully implemented real-time sentence decoding leveraging the syllable decoder.

Conclusions:

  • The developed tonally integrated, direct syllable neural decoding approach is effective for Mandarin Chinese.
  • This BCI system shows promise for restoring communication in tonal monosyllabic language speakers.
  • Paves the way for comprehensive communication systems in languages with similar structures.