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EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints.

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Τίτλος: EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints.
Συγγραφείς: Kouti M; Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.; Department of Electrical Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Ahvaz, Iran., Ansari-Asl K; Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. karim.ansari@scu.ac.ir., Namjoo E; Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Πηγή: Medical & biological engineering & computing [Med Biol Eng Comput] 2024 Oct; Vol. 62 (10), pp. 3073-3088. Date of Electronic Publication: 2024 May 21.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1741-0444 (Electronic) Linking ISSN: 01400118 NLM ISO Abbreviation: Med Biol Eng Comput Subsets: MEDLINE
Imprint Name(s): Publication: New York, NY : Springer
Original Publication: Stevenage, Eng., Peregrinus.
Ιατρικοί όροι (MeSH): Electroencephalography*/methods , Brain*/physiology , Brain*/diagnostic imaging , Algorithms*, Brain Mapping/methods ; Image Processing, Computer-Assisted/methods ; Humans ; Signal Processing, Computer-Assisted
Περίληψη: One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on INLINEMATH and INLINEMATH norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.
(© 2024. International Federation for Medical and Biological Engineering.)
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Grant Information: SCU.EE1401.82 Shahid Chamran University of Ahvaz
Contributed Indexing: Keywords: EEG source imaging; Non-stationary neural activity; Regularization; Spatio-temporal constraints
Entry Date(s): Date Created: 20240521 Date Completed: 20240906 Latest Revision: 20240906
Update Code: 20260130
DOI: 10.1007/s11517-024-03125-9
PMID: 38771431
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1741-0444
DOI:10.1007/s11517-024-03125-9