Compara mètodes

	Localització de fonts MEG	Modelatge Causal Dinàmic	eLORETA	Anàlisi de Potencials Relacionats amb Esdeveniments
Camp	Neuroimatge	Neuroimatge	Neuroimatge	Neuroimatge
Família	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	1972	2003	2002	1969
Autor original	David Cohen	Karl J. Friston	Roberto D. Pascual-Marqui	George Sutherland
Tipus	MEG neuroimaging analysis pipeline	Causal modeling pipeline for neuroimaging	EEG/MEG source localization algorithm	Time-locked EEG analysis pipeline
Font seminal	Hauk, O., Friston, K. J., & Leff, A. (2019). Functional neuroimaging of language: understanding the complex relationships between localization and function. Journal of Neurolinguistics, 50, 236–250.	Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302.	Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods & Findings in Experimental & Clinical Pharmacology, 24(S-D), 5–12.	Luck, S. J. (2005). An Introduction to the Event-Related Potential Technique. MIT Press.
Àlies	MEG localization, magnetic source imaging, MSI	DCM, Dynamic Causal Model	Exact LORETA, eLORETA source reconstruction	ERP, evoked potential, averaged EEG
Relacionats	3	2	2	3
Resum	Magnetoencephalography (MEG) source localization is the inverse problem of estimating where in the brain neural currents originate from magnetic field measurements at the scalp. Introduced by David Cohen in 1972, MEG offers superior temporal resolution (milliseconds) and spatial specificity compared to EEG, as magnetic fields are less distorted by tissue conductivity, enabling researchers to pinpoint neural activity with high precision.	Dynamic Causal Modeling (DCM) is a Bayesian framework for specifying and inverting generative models of brain connectivity from neuroimaging data. Introduced by Karl Friston and colleagues in 2003, DCM treats brain regions as dynamical systems and estimates effective connectivity by fitting observed fMRI time series to a biophysically plausible model of neuronal interactions.	Exact Low-Resolution Electromagnetic Tomography (eLORETA) is a non-parametric solution to the inverse problem in EEG and MEG source localization. Developed by Roberto D. Pascual-Marqui in 2002, eLORETA reconstructs three-dimensional maps of electrical brain activity from scalp electrode recordings, offering zero localization error under ideal noise-free conditions.	Event-Related Potential (ERP) analysis is a method for extracting stereotyped brain electrical responses time-locked to stimulus presentation or behavioral events from EEG recordings. Formalized in the cognitive neuroscience literature by researchers including Sutherland and Picton, ERP analysis enables millisecond-level temporal resolution of neural processing and has become foundational for studying perception, attention, memory, and decision-making.