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    Area of Science:

    • Neuroscience
    • Biophysics
    • Signal Processing

    Background:

    • Magnetoencephalography (MEG) signals are often contaminated by artifacts and external interference, even within shielded rooms.
    • Existing methods like signal-space projection (SSP) and signal-space separation (SSS) may not sufficiently suppress strong or time-varying interference.

    Purpose of the Study:

    • To introduce and evaluate a novel interference suppression technique for MEG data.
    • To demonstrate the effectiveness of extended signal-space separation (eSSS) in improving signal quality.

    Main Methods:

    • Developed extended signal-space separation (eSSS), integrating a physical model of magnetic fields with statistical interference description.
    • Validated eSSS performance using computational simulations and experimental MEG data.

    Main Results:

    • eSSS demonstrated superior interference suppression compared to SSS and SSP across various conditions.
    • The method showed no introduced bias in dipole modeling for location or amplitude.
    • eSSS successfully reduced external interference to the level of sensor noise.

    Conclusions:

    • eSSS offers enhanced data quality for MEG, outperforming current standard methods.
    • The technique is compatible with other SSS-based methods, expanding interference suppression capabilities for MEG.
    • eSSS facilitates advanced analyses like single-trial data processing, particularly in challenging environments.