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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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Considerations for implanting speech brain computer interfaces based on functional magnetic resonance imaging.

F Guerreiro Fernandes1, M Raemaekers1, Z Freudenburg1

  • 1Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands.

Journal of Neural Engineering
|April 22, 2024
PubMed
Summary

This study found that brain-computer interfaces (BCIs) for speech can be decoded from the sensorimotor cortex (SMC). Optimal speech BCI implants should use surface electrodes on the ventral 50% of the SMC.

Keywords:
BCIdecodingfMRIsensorimotor cortexspeech

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Speech Technology

Background:

  • Brain-computer interfaces (BCIs) offer a pathway to restore communication for individuals with speech loss.
  • Previous studies suggest sensorimotor cortex (SMC) activity can support speech decoding via subdural electrodes.
  • Optimal electrode placement and characteristics for speech BCIs remain largely undetermined.

Purpose of the Study:

  • To investigate the optimal characteristics of sensorimotor cortex (SMC) implants for speech brain-computer interfaces (BCIs).
  • To assess speech decodability based on hemisphere, gyrus, sulcal depth, and ventral/dorsal position within the SMC using high-field fMRI.
  • To identify specific brain regions within the SMC that yield the highest speech information.

Main Methods:

  • A 7 Tesla functional magnetic resonance imaging (fMRI) experiment was conducted with twelve subjects.
  • Subjects pronounced six distinct pseudo-words across multiple runs.
  • Multiclass support vector machine (SVM) classification was applied to divided SMC regions to decode spoken words.

Main Results:

  • Significant speech classification was achieved from the SMC, with no hemispheric or gyral preference.
  • Speech decoding was more effective using cortical surface data compared to deep sulcal data.
  • Classification accuracy was highest in the ventral 50% of the SMC.
  • SVM-searchlight analysis identified additional decoding sites in the superior temporal gyrus and left planum temporale.

Conclusions:

  • Results support the use of unilateral surface electrodes targeting the ventral 50% of the SMC for speech BCIs.
  • The benefit of depth electrodes for speech decoding in the SMC is currently unclear.
  • Further validation in paralyzed patients attempting speech is warranted.