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.
|Title of host publication||Proceedings of NIPS 99|
|Place of Publication||Denver|
|Publication status||Published - 2000|
|Event||Proceedings of NIPS 99, - Denver|
Duration: 1 Jan 1999 → …
|Conference||Proceedings of NIPS 99,|
|Period||01/01/1999 → …|