ArticleFrontiers in neuroscience2021
Correcting MEG Artifacts Caused by Overt Speech.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 22 citations in OpenAlex.
- Spatiotemporal dynamics of lexical and morphosyntactic planning during language production revealed by magnetoencephalography.bioRxiv : the preprint server for biology · 2026Article
- Neural dynamics and acoustic adaptations during the Lombard effect: evidence from EEG and dynamic causal modeling.Scientific reports · 2026Article
- Neural and autonomic regulation during brief mindfulness and relaxation interventions in clinical populations: a multimodal MEG study protocol.Frontiers in neuroscience · 2026Article
- Automatic Removal of Physiological Artifacts in OPM-MEG: A Framework of Channel Attention Mechanism Based on Magnetic Reference Signal.Biosensors · 2025Article
- Automated speech artefact removal from MEG data utilizing facial gestures and mutual information.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Frequency-specific cortico-subcortical interaction in continuous speaking and listening.eLife · 2024Article
- Spatiotemporal dynamics characterise spectral connectivity profiles of continuous speaking and listening.PLoS biology · 2023Article
- Picture-Word Interference Effects Are Robust With Covert Retrieval, With and Without Gamification.Frontiers in psychology · 2021Article
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recording brain activity during speech production using magnetoencephalography (MEG) can help us to understand the dynamics of speech production. However, these measurements are challenging due to the induced artifacts coming from several sources such as facial muscle activity, lower jaw and head movements. Here, we aimed to characterize speech-related artifacts, focusing on head movements, and subsequently present an approach to remove these artifacts from MEG data. We recorded MEG from 11 healthy participants while they pronounced various syllables in different loudness. Head positions/orientations were extracted during speech production to investigate its role in MEG distortions. Finally, we present an artifact rejection approach using the combination of regression analysis and signal space projection (SSP) in order to correct the induced artifact from MEG data. Our results show that louder speech leads to stronger head movements and stronger MEG distortions. Our proposed artifact rejection approach could successfully remove the speech-related artifact and retrieve the underlying neurophysiological signals. As the presented artifact rejection approach was shown to remove artifacts arising from head movements, induced by overt speech in the MEG, it will facilitate research addressing the neural basis of speech production with MEG.
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