An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel Counts
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 intern...
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| Format: | Article |
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MDPI AG
2023-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/14/6537 |
| _version_ | 1797587513695535104 |
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| author | Niall Holmes Richard Bowtell Matthew J Brookes Samu Taulu |
| author_facet | Niall Holmes Richard Bowtell Matthew J Brookes Samu Taulu |
| author_sort | Niall Holmes |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:40:00Z |
| format | Article |
| id | doaj.art-5a83af40a641433fa596fff8854ea393 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:40:00Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5a83af40a641433fa596fff8854ea3932023-11-18T21:19:06ZengMDPI AGSensors1424-82202023-07-012314653710.3390/s23146537An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel CountsNiall Holmes0Richard Bowtell1Matthew J Brookes2Samu Taulu3Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UKSir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UKSir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UKDepartment of Physics, University of Washington, Seattle, WA 98195, USAThe 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.https://www.mdpi.com/1424-8220/23/14/6537optically pumped magnetometermagnetoencephalographySSSMEG analysis |
| spellingShingle | Niall Holmes Richard Bowtell Matthew J Brookes Samu Taulu An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel Counts Sensors optically pumped magnetometer magnetoencephalography SSS MEG analysis |
| title | An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel Counts |
| title_full | An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel Counts |
| title_fullStr | An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel Counts |
| title_full_unstemmed | An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel Counts |
| title_short | An Iterative Implementation of the Signal Space Separation Method for Magnetoencephalography Systems with Low Channel Counts |
| title_sort | iterative implementation of the signal space separation method for magnetoencephalography systems with low channel counts |
| topic | optically pumped magnetometer magnetoencephalography SSS MEG analysis |
| url | https://www.mdpi.com/1424-8220/23/14/6537 |
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