Data-driven beamforming techniques to attenuate ballistocardiogram (BCG) artefacts in EEG-fMRI without detecting cardiac pulses in electrocardiography (ECG) recordings
Notice bibliographique
Résumé
Abstract Simultaneous recording of EEG and fMRI is a very promising non-invasive neuroimaging technique, providing a wide range of complementary information to characterize underlying mechanisms associated with brain functions. However, EEG data obtained from the simultaneous EEG-fMRI recordings are strongly influenced by MRI related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. The GA is induced by temporally varying magnetic field gradients used for MR imaging, whereas the BCG artefacts are produced by cardiac pulse driven head motion in the strong magnetic field of the MRI scanner, so that this BCG artefact will be present when the subject is lying in the scanner, even when no fMRI data are acquired. When compared to corrections of the GA, the BCG artefact corrections are more challenging to remove due to its inherent variabilities and dynamic changes over time. Typically, the BCG artefacts obscure the EEG signals below 20Hz, and this remains problematic especially when the frequency of interest of EEG signals is below 20Hz, such as Alpha (8-13Hz) and Beta (13-30Hz) band EEG activity, or sleep spindle (11-16Hz) and slow-wave oscillations (<1 Hz) during sleep. The standard BCG artefact corrections, as for instance Average Artefact Subtraction method (AAS), require detecting cardiac pulse (R-peak) events from simultaneous electrocardiography (ECG) recordings. However, ECG signals in the MRI scanner are sometimes distorted and will become problematic for detecting reliable R-peaks. In this study, we focused on a beamforming technique, which is a spatial filtering technique to reject sources of signal variance that do not appear dipolar in the source space. This technique attenuates all unwanted source activities outside of a presumed region of interest without having to specify the location or the configuration of these underlying source signals. Specifically, in this study, we revisited the advantages of the beamforming technique to attenuate the BCG artefact in EEG-fMRI, and also to recover meaningful task-based induced neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond as accurately and quickly as possible. We analysed EEG-fMRI data in 20 healthy participants when they were performing the ANT, and compared four different BCG correction approaches (non-BCG corrected, AAS BCG corrected, beamforming+AAS BCG corrected, beamforming BCG corrected). We demonstrated that beamforming BCG corrected data did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task-based induced brain activity when compared to the standard AAS BCG corrections. Without detecting R-peak events from the ECG, this data-driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG-fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI related artefacts. Highlights Beamforming spatial filtering technique attenuates ballistocardiogram (BCG) artefacts in EEG-fMRI without detecting cardiac pulses in electrocardiography (ECG) recordings. Beamforming BCG denoising technique recovers expected task-based induced visual alpha and motor beta event-related desynchronization (ERD). Beamforming technique improves signal-noise ratios (SNR) of neural activities as compared to sensor level signals. Data-driven beamforming technique appears promising for longer data acquisition of sleep and resting EEG-fMRI without relying on ECG signals.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».