ArticleNeural computation2021
Bridging M/EEG Source Imaging and Independent Component Analysis Frameworks Using Biologically Inspired Sparsity Priors.
Article in Neural computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
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- Brief mindfulness coaching enhances selective attention in medical scientists: A pilot study.PloS one · 2025Article
- Design and Implementation of a Brief Digital Mindfulness and Compassion Training App for Health Care Professionals: Cluster Randomized Controlled Trial.JMIR mental health · 2024Article
- Neural activity during inhibitory control predicts suicidal ideation with machine learning.NPP - digital psychiatry and neuroscience · 2024Article
- Distinct neural activations correlate with maximization of reward magnitude versus frequency.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Dissociable neural mechanisms of cognition and well-being in youth versus healthy aging.Psychology and aging · 2022Article
- Utility of Cognitive Neural Features for Predicting Mental Health Behaviors.Sensors (Basel, Switzerland) · 2022Article
- Neural dynamics during emotional video engagement relate to anxiety.Frontiers in human neuroscience · 2022Article
- Personalized machine learning of depressed mood using wearables.Translational psychiatry · 2021Article
- Chronic, Multi-Site Recordings Supported by Two Low-Cost, Stationary Probe Designs Optimized to Capture Either Single Unit or Local Field Potential Activity in Behaving Rats.Frontiers in psychiatry · 2021Article
- Electrophysiological Correlates of Rodent Default-Mode Network Suppression Revealed by Large-Scale Local Field Potential Recordings.Cerebral cortex communications · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
Electromagnetic source imaging (ESI) and independent component analysis (ICA) are two popular and apparently dissimilar frameworks for M/EEG analysis. This letter shows that the two frameworks can be linked by choosing biologically inspired source sparsity priors. We demonstrate that ESI carried out by the sparse Bayesian learning (SBL) algorithm yields source configurations composed of a few active regions that are also maximally independent from one another. In addition, we extend the standard SBL approach to source imaging in two important directions. First, we augment the generative model of M/EEG to include artifactual sources. Second, we modify SBL to allow for efficient model inversion with sequential data. We refer to this new algorithm as recursive SBL (RSBL), a source estimation filter with potential for online and offline imaging applications. We use simulated data to verify that RSBL can accurately estimate and demix cortical and artifactual sources under different noise conditions. Finally, we show that on real error-related EEG data, RSBL can yield single-trial source estimates in agreement with the experimental literature. Overall, by demonstrating that ESI can produce maximally independent sources while simultaneously localizing them in cortical space, we bridge the gap between the ESI and ICA frameworks for M/EEG analysis.
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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.