STARTS: A self-adapted spatio-temporal framework for automatic E/MEG source imaging.

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To obtain accurate brain source activities, the highly ill-posed source imaging of electro- and magneto-encephalography (E/MEG) requires proficiency in incorporation of biophysiological constraints and signal-processing techniques. Here, we propose a spatio-temporal-constrained E/MEG source imaging framework-STARTS that can reconstruct the source in a fully automatic way. Specifically, a block-diagonal covariance is adopted to reconstruct the source extents while maintain spatial homogeneity. Temporal basis functions (TBFs) of both sources and noise are estimated and updated in a data-driven fashion to alleviate the influence of noises and further improve source localization accuracy. The performance of the proposed STARTS is quantitatively assessed through a series of simulation experiments, wherein superior results are obtained in comparison with the benchmark ESI algorithms (including LORETA, EBI-Convex, SI-STBF, &BESTIES). Additional validations on epileptic and resting-state EEG data further indicate that the STARTS can produce neurophysiologically plausible results. Moreover, a computationally efficient version of STARTS: smooth STARTS is also introduced with an elementary spatial constraint, which exhibited comparable performance and reduced execution cost. In sum, the proposed STARTS, with its advanced spatio-temporal constraints and self-adapted update operation, provides an effective and efficient approach for E/MEG source imaging.