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Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

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

Victoria Peterson^1,2, Matteo Vissani¹, Shiyu Luo³

¹Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States.

Biorxiv : the Preprint Server for Biology

|April 17, 2023

Summary

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

Keywords:

Phase-Coupling Optimization Spatial Filtering Speech Artifact Speech Production iEEG

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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Published on: April 16, 2010

Area of Science:

Neuroscience
Signal Processing

Background:

Direct brain recordings in awake patients offer unique insights into human speech neurophysiology.
Intracranial electroencephalography (iEEG) signals during overt speech are susceptible to acoustic artifacts.
Speech artifacts, tracking fundamental frequency (F0), overlap with high-gamma neural activity.

Approach:

Developed a data-driven spatial-filtering method to identify and remove acoustic-induced speech artifacts.
Compared the novel method against traditional reference schemes for iEEG signal analysis.
Focused on preserving underlying neural activity while denoising recordings.

Key Points:

Acoustic speech artifacts contaminate iEEG signals during overt speech.
Traditional reference schemes can compromise signal quality.
The developed spatial-filtering approach effectively denoises iEEG recordings.

Conclusions:

The novel spatial-filtering method successfully removes speech artifacts from iEEG data.
This technique preserves neural activity essential for studying speech production and perception.
The approach enhances the rigor of analyzing neurophysiological data from awake neurosurgical patients.