Evidence map›Paper›PMID 40800288›Full record

ArticleImaging neuroscience (Cambridge, Mass.)2024

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

Victoria Peterson, Matteo Vissani, Shiyu Luo, Qinwan Rabbani, Nathan E Crone, Alan Bush, R Mark Richardson

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In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Victoria PetersonDepartment of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Matteo VissaniDepartment of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Shiyu LuoDepartment of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Qinwan RabbaniDepartment of Electrical & Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States.
Nathan E CroneDepartment of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Alan BushDepartment of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
R Mark RichardsonDepartment of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurosurgical procedures that enable direct brain recordings in awake patients offer unique opportunities to explore the neurophysiology of human speech. The scarcity of these opportunities and the altruism of participating patients compel us to apply the highest rigor to signal analysis. Intracranial electroencephalography (iEEG) signals recorded during overt speech can contain a speech artifact that tracks the fundamental frequency (F0) of the participant's voice, involving the same high-gamma frequencies that are modulated during speech production and perception. To address this artifact, we developed a spatial-filtering approach to identify and remove acoustic-induced contaminations of the recorded signal. We found that traditional reference schemes jeopardized signal quality, whereas our data-driven method denoised the recordings while preserving underlying neural activity.

Indexed as

iEEGphase-coupling optimizationspatial filteringspeech artifactspeech production

Identifiers

PMID40800288
PMCPMC12290705

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.