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Updated: Jul 24, 2025

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Overt speech decoding from cortical activity: a comparison of different linear methods.

Gaël Le Godais1, Philémon Roussel1, Florent Bocquelet1

  • 1Univ. Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble, France.

Frontiers in Human Neuroscience
|July 10, 2023
PubMed
Summary
This summary is machine-generated.

Linear decoders show promise for real-time speech Brain-Computer Interfaces (BCIs), decoding cortical activity into speech. While performance was above chance, further development is needed for intelligible speech reconstruction.

Keywords:
ECoGarticulatory synthesisbrain-computer interfacedecodingintracranial recordingslinear methodsspeech prostheses

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Speech Brain-Computer Interfaces (BCIs) aim to reconstruct speech in real-time from cortical activity.
  • Fast, millisecond-timescale reconstruction requires efficient computation, making linear decoders attractive.
  • Linear decoders are well-established in motor BCIs but underexplored for speech and articulatory movement decoding.

Purpose of the Study:

  • To compare linear regression methods for offline decoding of overt speech from cortical activity.
  • To investigate direct acoustic decoding versus indirect decoding via articulatory movements.
  • To evaluate the performance of different linear decoding strategies for speech reconstruction.

Main Methods:

  • Compared vanilla linear regression, ridge-regularized linear regressions, and partial least squares regressions.
  • Investigated two decoding paradigms: direct acoustic feature decoding and indirect decoding through articulatory representations.
  • Used dynamic time warping to estimate articulatory trajectories and evaluated decoder accuracy via feature correlations.

Main Results:

  • All linear methods achieved performance significantly above chance levels.
  • Direct and indirect decoding paradigms showed comparable performance, with a slight advantage for direct decoding.
  • Current linear methods did not achieve intelligible speech reconstruction.

Conclusions:

  • Linear decoders are viable for initial speech reconstruction from cortical activity.
  • Further research is needed to improve neural speech decoders for millisecond-timescale, intelligible speech.
  • Optimizing decoders for fast, frame-by-frame reconstruction remains a key challenge for speech BCIs.