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

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 signalprocessing techniques. Here, we propose a spatiotemporal-constrainted E/MEG source imaging framework-STARTS that can reconstruct the source in a fully automatic way. Specifically, a block diagonal covariance was adopted to reconstruct the source extents while maintain spatial homogeneity. Temporal basis functions (TBFs) of both sources and noises were 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 was quantitatively assessed through a series of simulation experiments,wherein superior results were obtained in comparison with the benchmark ESI algorithms (including LORETA, EBIConvex,& SISTBF). 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 was 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.