Robust interpolation of EEG/MEG sensor time-series via electromagnetic source imaging

Abstract Objective: Electroencephalography (EEG) and Magnetoencephalography (MEG) are widely used non-invasive techniques in clinical and cognitive neuroscience. However, low spatial resolution measurements, partial brain coverage by some sensor arrays, as well as noisy sensors could result in distorted sensor topographies resulting in inaccurate reconstructions of underlying brain dynamics. Solving these problems has been a challenging task, This paper proposes a robust framework based on electromagnetic source imaging for interpolation of unknown or poor quality EEG/MEG measurements. Approach: This framework consists of two steps: 1) estimating brain source activity using a robust inverse algorithm along with the leadfield matrix of available good sensors, and 2) interpolating unknown or poor quality EEG/MEG measurements using the reconstructed brain sources using the leadfield matrices of unknown or poor quality sensors. We evaluate the proposed framework through simulations and several real datasets, comparing its performance to two popular benchmarks - neighborhood interpolation (NI) and spherical spline interpolation (SSI) algorithms. Results: In both simulations and real EEG/MEG measurements, we demonstrate several advantages compared to benchmarks, which are robust to highly correlated brain activity, low signal-to-noise ratio data and accurately estimates cortical dynamics. Significance: These results demonstrate a rigorous platform to enhance the spatial resolution of EEG and MEG, to overcome limitations of partial coverage of EEG/MEG sensor arrays that is particularly relevant to low-channel count optically pumped magnetometer (OPM) arrays, and to estimate activity in poor/noisy sensors to a certain extent based on the available measurements from other good sensors. Implementation of this framework will enhance the quality of EEG and MEG, thereby expanding the potential applications of these modalities.