Electrophysiology recordings are frequently affected by artifacts (e.g., subject motion or eye movements), which reduces the number of available trials and affects the statistical power. When artifacts are unavoidable and data are scarce, signal reconstruction algorithms that allow for the retention of sufficient trials become crucial.
In our paper published on Sensors, in collaboration with Ghislaine Dehaene and Shruti Naik, we present one such algorithm that makes use of large spatiotemporal correlations in neural signals and solves a low-rank matrix completion problem, akin to compressed sensing, to fix artifactual entries. The idea is that multivariate time-series of neural activity sample a low-dimensional manifolds. Therefore, observations at different locations in space and time are not independent because of this heavy constraint. This fact can be exploited to provide good guesses of missing chunks of data, in a way which goes beyond mere local interpolation of individual time-series.
The method uses a gradient descent algorithm in lower dimensions to learn the missing entries in a supposedly low-rank matrix and provide faithful reconstruction of signals. We carried out numerical simulations to benchmark the method and estimate optimal hyperparameters for actual EEG data. The fidelity of reconstruction was assessed by detecting event-related potentials (ERP) from a highly artifacted EEG time series from human infants. The proposed method significantly improved the standardized error of the mean in ERP group analysis and a between-trial variability analysis compared to a state-of-the-art interpolation technique. This improvement increased the statistical power and revealed significant effects that would have been deemed insignificant without reconstruction.
The method can be applied to any time-continuous neural signal where artifacts are sparse and spread out across epochs and channels, increasing data retention and statistical power.
To know more:
- Naik, S., Dehaene-Lambertz, G., and Battaglia, D. (2023). Repairing Artifacts in Neural Activity Recordings Using Low-Rank Matrix Estimation. Sensors 23, 4847. 10.3390/s23104847.