Linganisha mbinu

	Uamuzi wa Chanzo cha MEG	Uchanganuzi wa Kina wa Sababu	Uchambuzi wa Matukio Yanayohusiana na Potenshali
Nyanja	Upigaji Picha wa Ubongo	Upigaji Picha wa Ubongo	Upigaji Picha wa Ubongo
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	1972	2003	1969
Mwanzilishi	David Cohen	Karl J. Friston	George Sutherland
Aina	MEG neuroimaging analysis pipeline	Causal modeling pipeline for neuroimaging	Time-locked EEG analysis pipeline
Chanzo asilia	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.	Luck, S. J. (2005). An Introduction to the Event-Related Potential Technique. MIT Press.
Majina mbadala	MEG localization, magnetic source imaging, MSI	DCM, Dynamic Causal Model	ERP, evoked potential, averaged EEG
Zinazohusiana	3	2	3
Muhtasari	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.	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.