ArticleNeuroImage2009
MEG and EEG data fusion: simultaneous localisation of face-evoked responses.
Article in NeuroImage, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed.
- Kymata Soto Language Dataset: an electro-magnetoencephalographic dataset for natural speech processing.Scientific data · 2026Article
- The Mismatch Negativity Compared: EEG, SQUID-MEG, and NovelHuman brain mapping · 2025Article
- Word-selective EEG/MEG responses in the English language obtained with fast periodic visual stimulation (FPVS).Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Joint estimation of source dynamics and interactions from MEG data.Network neuroscience (Cambridge, Mass.) · 2025Article
- Aperiodic and oscillatory systems underpinning human domain-general cognition.Communications biology · 2024Article
- Article
- Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
- Hearing what is being said: the distributed neural substrate for early speech interpretation.Language, cognition and neuroscience · 2024Article
- Parkinson's disease impairs cortical sensori-motor decision-making cascades.Brain communications · 2024Article
- Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes.NeuroImage · 2023Article
- Distinct roles for the anterior temporal lobe and angular gyrus in the spatiotemporal cortical semantic network.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Neurophysiological and Brain Structural Markers of Cognitive Frailty Differ from Alzheimer's Disease.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Neurocomputational Underpinnings of Expected Surprise.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Combining electro- and magnetoencephalography data using directional archetypal analysis.Frontiers in neuroscience · 2022Article
- Maturational trajectory of fusiform gyrus neural activity when viewing faces: From 4 months to 4 years old.Frontiers in human neuroscience · 2022Article
- Imaging the extent and location of spatiotemporally distributed epileptiform sources from MEG measurements.NeuroImage. Clinical · 2022Article
- Face-selective responses in combined EEG/MEG recordings with fast periodic visual stimulation (FPVS).NeuroImage · 2021Article
- Ant Colony System Optimization for Spatiotemporal Modelling of Combined EEG and MEG Data.Entropy (Basel, Switzerland) · 2021Article
- Tau pathology in early Alzheimer's disease is linked to selective disruptions in neurophysiological network dynamics.Neurobiology of aging · 2020Article
- Anterior temporal lobe is necessary for efficient lateralised processing of spoken word identity.Cortex; a journal devoted to the study of the nervous system and behavior · 2020Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
We present an empirical Bayesian scheme for distributed multimodal inversion of electromagnetic forward models of EEG and MEG signals. We used a generative model with common source activity and separate error components for each modality. Under this scheme, the weightings of error for each modality, relative to source components, are estimated automatically from the data, by optimising the model-evidence. This obviates the need for arbitrary user-defined weightings. To evaluate the scheme, we acquired three types of data simultaneously from twelve participants: total magnetic flux (as recorded by 102 magnetometers), orthogonal in-plane gradients of the magnetic field (as recorded by 204 planar gradiometers) and voltage differences in the electrical field (recorded by 70 electrodes). We assessed the relative precision of each sensor-type in terms of signal-to-noise ratio (SNR); using empirical sample variances and optimised estimators from the generative model. We then compared the localisation of face-evoked responses, using each modality separately, with that obtained by their "fusion" under the common generative model. Finally, we quantified the conditional precisions of the source estimates using their posterior covariance, confirming that EEG can improve MEG-based source reconstructions.
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Registered trials
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.