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Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements
Abstract:
Limitations of traditional magnetoencephalography (MEG) exclude some important patient groups from MEG examinations, such as epilepsy patients with a vagus nerve stimulator, patients with magnetic particles on the head or having magnetic dental materials that cause severe movement-related artefact signals. Conventional interference rejection methods are not able to remove the artefacts originating this close to the MEG sensor array. For example, the reference array method is unable to suppress interference generated by sources closer to the sensors than the reference array, about 20-40 cm. The spatiotemporal signal space separation method proposed in this paper recognizes and removes both external interference and the artefacts produced by these nearby sources, even on the scalp. First, the basic separation into brain-related and external interference signals is accomplished with signal space separation based on sensor geometry and Maxwell's equations only. After this, the artefacts from nearby sources are extracted by a simple statistical analysis in the time domain, and projected out. Practical examples with artificial current dipoles and interference sources as well as data from real patients demonstrate that the method removes the artefacts without altering the field patterns of the brain signals.
Insights
This study introduces a new spatiotemporal signal space separation method to remove artifacts in magnetoencephalography (MEG) scans. This technique allows more patients, including those with medical implants, to undergo crucial MEG examinations.
Area of Science:
- Biophysics
- Neuroscience
- Biomedical Engineering
Background:
- Traditional magnetoencephalography (MEG) is limited by artifacts from nearby sources, excluding patient groups with implants or dental materials.
- Existing interference rejection methods fail to remove artifacts originating close to MEG sensors (20-40 cm).
Purpose of the Study:
- To develop and validate a novel spatiotemporal signal space separation (STSSS) method for artifact removal in MEG.
- To enable broader patient access to MEG examinations by overcoming current limitations.
Main Methods:
- Implemented STSSS using sensor geometry and Maxwell's equations for initial brain and external signal separation.
- Applied statistical analysis in the time domain to identify and remove nearby artifacts, including those on the scalp.
- Validated the method using artificial current dipoles, interference sources, and real patient data.
Main Results:
- The STSSS method effectively recognizes and removes both external interference and artifacts from nearby sources.
- Artifacts originating on the scalp were successfully suppressed without distorting brain signal patterns.
- Demonstrated artifact removal in practical examples with artificial and real patient data.
Conclusions:
- The proposed STSSS method significantly enhances MEG's applicability by overcoming artifact limitations.
- This technique expands the potential patient population for MEG studies, including those previously excluded.
- The method preserves the integrity of brain signals while effectively removing unwanted interference.
