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Pemodelan Kausal Dinamis×Analisis Potensial Terkait Peristiwa×
BidangPencitraan SarafPencitraan Saraf
KeluargaProcess / pipelineProcess / pipeline
Tahun asal20031969
PencetusKarl J. FristonGeorge Sutherland
TipeCausal modeling pipeline for neuroimagingTime-locked EEG analysis pipeline
Sumber perintisFriston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302. DOI ↗Luck, S. J. (2005). An Introduction to the Event-Related Potential Technique. MIT Press. link ↗
AliasDCM, Dynamic Causal ModelERP, evoked potential, averaged EEG
Terkait23
RingkasanDynamic 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.
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ScholarGateBandingkan metode: Dynamic Causal Modeling · Event-Related Potential Analysis. Diakses 2026-06-19 dari https://scholargate.app/id/compare