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A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological

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Summary

Researchers developed a novel spatial-filtering method to remove speech artifacts from intracranial electroencephalography (iEEG) recordings. This technique successfully denoises brain signals during speech, preserving crucial neural activity for speech neurophysiology studies.

Keywords:
iEEGphase-coupling optimizationspatial filteringspeech artifactspeech production

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Direct brain recordings in awake patients offer unique insights into human speech neurophysiology.
  • Intracranial electroencephalography (iEEG) signals are susceptible to speech artifacts, particularly those related to fundamental frequency (F0).
  • These artifacts overlap with high-gamma frequencies critical for speech production and perception.

Purpose of the Study:

  • To develop and validate a spatial-filtering approach for identifying and removing acoustic-induced speech artifacts from iEEG recordings.
  • To improve the signal quality of iEEG data acquired during overt speech in neurosurgical patients.
  • To preserve authentic neural activity while effectively denoising speech-related contaminations.

Main Methods:

  • A novel spatial-filtering technique was developed to isolate and remove speech artifacts.
  • The method focuses on identifying and subtracting acoustic-induced signal contaminations.
  • Data-driven approaches were employed to optimize artifact removal without compromising neural signals.

Main Results:

  • The developed spatial-filtering method effectively identified and removed speech artifacts from iEEG signals.
  • Traditional reference schemes were found to degrade signal quality.
  • The data-driven spatial-filtering approach successfully denoised recordings while preserving underlying neural activity.

Conclusions:

  • Acoustic-induced speech artifacts in iEEG can be effectively mitigated using a spatial-filtering approach.
  • This data-driven method offers a superior alternative to traditional reference schemes for denoising iEEG during speech.
  • The technique enhances the reliability of iEEG data for studying human speech neurophysiology.