MEG recordings of DC fields using the signal space separation method (SSS)

Area of Science:

• Biophysics
• Biomagnetism
• Neuroimaging

Background:

• Conventional magnetoencephalography (MEG) using stationary sensors cannot detect direct current (DC) sources.
• DC sources, like physiological currents or magnetic impurities, are typically invisible in standard MEG recordings.
• Subject movement relative to sensors can convert DC fields into measurable time-varying MEG signals, posing challenges as artifacts or potential signals.

Purpose of the Study:

• To develop and validate a novel method for localizing DC sources using MEG.
• To overcome the limitations of stationary sensors in detecting DC magnetic fields.
• To differentiate between biomagnetic DC sources and movement artifacts in MEG.

Main Methods:

• Utilized the signal space separation (SSS) method combined with continuous head position monitoring.
• Developed a technique to demodulate DC fields into time-varying MEG signals by tracking head movements.
• Formulated a linear equation to separate DC sources from external interference based on signal variations.

Main Results:

• Successfully localized a DC current dipole in a phantom head with 2 mm accuracy.
• Demonstrated the method's effectiveness with random, multi-centimeter movements of the phantom.
• Showed that the SSS-based movement demodulation accurately reconstructs signals and isolates DC sources.

Conclusions:

• The developed method enables the localization of DC sources using standard MEG.
• Voluntary head movements can be leveraged to reveal and pinpoint DC biomagnetic or artifactual sources.
• This advancement expands the capabilities of MEG for investigating DC physiological processes and artifacts.