ArticleSensors (Basel, Switzerland)2023
An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel Counts.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Noise Optimization of Basic Signal Component Extraction for Cryogenic and On-Scalp Magnetoencephalography (MEG).bioRxiv : the preprint server for biology · 2026Article
- Optimizing OPM-MEG Sensor Layouts Using the Sequential Selection Algorithm with Simulated Sources and Individual Anatomy.Sensors (Basel, Switzerland) · 2026Article
- Potential ofEpilepsia open · 2025Article
- OPM-MEG reveals dynamics of beta bursts underlying attentional processes in sensory cortex.Scientific reports · 2025Article
- SPM-30 years and beyond.Cerebral cortex (New York, N.Y. : 1991) · 2025Review
- Wearable Magnetoencephalography in a Lightly Shielded Environment.IEEE transactions on bio-medical engineering · 2025Article
- Pushing the boundaries of MEG based on optically pumped magnetometers towards early human life.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Determining sensor geometry and gain in a wearable MEG system.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Optimizing magnetometers arrays and analysis pipelines for multivariate pattern analysis.Journal of neuroscience methods · 2024Article
- Automatic Estimation of the Interference Subspace Dimension Threshold in the Subspace Projection Algorithms of Magnetoencephalography Based on Evoked State Data.Bioengineering (Basel, Switzerland) · 2024Article
- Adaptive multipole models of optically pumped magnetometer data.Human brain mapping · 2024Article
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Authors and funding
4 authors.
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Abstract
The signal space separation (SSS) method is routinely employed in the analysis of multichannel magnetic field recordings (such as magnetoencephalography (MEG) data). In the SSS method, signal vectors are posed as a multipole expansion of the magnetic field, allowing contributions from sources internal and external to a sensor array to be separated via computation of the pseudo-inverse of a matrix of the basis vectors. Although powerful, the standard implementation of the SSS method on MEG systems based on optically pumped magnetometers (OPMs) is unstable due to the approximate parity of the required number of dimensions of the SSS basis and the number of channels in the data. Here we exploit the hierarchical nature of the multipole expansion to perform a stable, iterative implementation of the SSS method. We describe the method and investigate its performance via a simulation study on a 192-channel OPM-MEG helmet. We assess performance for different levels of truncation of the SSS basis and a varying number of iterations. Results show that the iterative method provides stable performance, with a clear separation of internal and external sources.
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