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Learning discrete neural latent spaces for high-performance speech decoding.

Yao Jia1, Qi Lian2, Lei Wang3

  • 1Affiliated Mental Health Center Hangzhou Seventh People's Hospital, and MOE Frontier Science Center for Brain Science and Brain-machine Integration, and College of Computer Science and Technology, Zhejiang University, 38#, Zheda Road, Hangzhou, Zhejiang province, 310058, CHINA.

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Summary
This summary is machine-generated.

This study introduces a novel discrete neural latent space for speech brain-computer interfaces (BCIs). This approach enhances speech decoding precision and robustness for individuals with aphasia.

Keywords:
Brain-computer interfacediscrete neural latent spaceintracranial recordingsspeech decoding

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Speech brain-computer interfaces (BCIs) offer a communication pathway for individuals with aphasia.
  • Neural representation learning is crucial for decoding speech from brain signals.

Purpose of the Study:

  • To develop a novel approach for speech decoding using discrete neural latent spaces.
  • To improve the precision and robustness of speech BCIs.

Main Methods:

  • Proposed a quantized representation learning network to learn discrete neural latent spaces.
  • Utilized intracranial stereotactic EEG (sEEG) signals from 11 subjects for experiments.

Main Results:

  • The proposed method significantly enhanced the precision of speech decoding.
  • Demonstrated improved robustness in speech decoding compared to existing methods.

Conclusions:

  • The discrete neural latent space approach holds significant potential for advancing speech BCIs.
  • This method can improve the functionality and usability of BCIs for people with aphasia.