Effectively combining temporal projection noise suppression methods in magnetoencephalography

Magnetoencephalography (MEG) is an excellent non-invasive tool to study the brain. However, measurements often suffer from the contribution of external interference, including noise from the sensors. Suppression of noise from the data is critical for an accurate representation of brain signals. Due to MEG's limited spatial resolution and superior temporal resolution, noise suppression methods that operate in the temporal domain can be favorable.

We examined the independent and joint effects of two temporal projection noise suppression algorithms for MEG measurements: One commonly used algorithm which suppresses correlated noise; temporal signal space separation (tSSS) and one new method which suppresses uncorrelated sensor noise; oversampled temporal projection (OTP).

We found that both OTP and tSSS effectively suppress noise in raw MEG data and have the greatest effect of joint operation in cases where SNR is low, or when detecting higher SNR single-trial responses from raw data. We additionally demonstrate how the combination of OTP and tSSS is useful for the detectability of high-frequency brain oscillations (HFO).

Although the mathematical description of OTP has been described before (Larson and Taulu, 2017), OTP's effect on HFOs in MEG data is novel. Additionally, the combination of OTP and commonly used temporal noise suppression algorithms (i.e., tSSS) has not been shown.

This finding is applicable to clinical populations such as epilepsy, where HFO signals are thought to be important markers for areas of seizure onset and are typically difficult to detect with non-invasive neuroimaging methods.