Abstract
We present a Hidden Markov Model (HMM) for inferring the hidden
psychological state (or neural activity) during single trial fMRI activation
experiments with blocked task paradigms. Inference is based on
Bayesian methodology, using a combination of analytical and a variety
of Markov Chain Monte Carlo (MCMC) sampling techniques. The advantage
of this method is that detection of short time learning effects between
repeated trials is possible since inference is based only on single
trial experiments.
Original language | English |
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Title of host publication | Proceedings of NIPS 99 |
Place of Publication | Denver |
Publication date | 2000 |
Pages | 754-760 |
Publication status | Published - 2000 |
Event | Neural Information Processing Systems 1999 - Denver, United States Duration: 29 Nov 1999 → 4 Dec 1999 |
Conference
Conference | Neural Information Processing Systems 1999 |
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Country/Territory | United States |
City | Denver |
Period | 29/11/1999 → 04/12/1999 |