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

AI Article Synopsis

  • - The STARTS framework improves E/MEG source imaging by automatically incorporating biophysiological constraints and advanced signal-processing techniques to accurately reconstruct brain activity.
  • - It uses a block-diagonal covariance to maintain spatial uniformity while updating noise and source estimates dynamically, enhancing localization accuracy.
  • - Experimental results show that STARTS outperforms existing algorithms, achieving more accurate neurophysiological results, and a simplified version called smooth STARTS offers similar performance with lower computational costs.

Article Abstract

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.

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Source
http://dx.doi.org/10.1109/TMI.2024.3483292DOI Listing

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