ArticleBrain topography2023
Optimization of Signal Space Separation for Optically Pumped Magnetometer in Magnetoencephalography.
Article in Brain topography, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- 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
- 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
7 authors.
Funding
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Abstract
Magnetoencephalography (MEG) is a noninvasive functional neuroimaging modality but highly susceptible to environmental interference. Signal space separation (SSS) is a method for improving the SNR to separate the MEG signals from external interference. The origin and truncation values of SSS significantly affect the SSS performance. The origin value fluctuates with respect to the helmet array, and determining the truncation values using the traversal method is time-consuming; thus, this method is inappropriate for optically pumped magnetometer (OPM) systems with flexible array designs. Herein, an automatic optimization method for the SSS parameters is proposed. Virtual sources are set inside and outside the brain to simulate the signals of interest and interference, respectively, via forward model, with the sensor array as prior information. The objective function is determined as the error between the signals from simulated sources inside the brain and the SSS reconstructed signals; thus, the optimized parameters are solved inversely by minimizing the objective function. To validate the proposed method, a simulation analysis and MEG auditory-evoked experiments were conducted. For an OPM sensor array, this method can precisely determine the optimized origin and truncation values of the SSS simultaneously, and the auditory-evoked component, for example, N100, can be accurately located in the temporal cortex. The proposed optimization procedure outperforms the traditional method with regard to the computation time and accuracy, simplifying the SSS process in signal preprocessing and enhancing the performance of SSS denoising.
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Registered trials
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