http://www.nitrc.org/forum/forum.php?forum_id=7681 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Multivariate Bayesian decoding of single-trial event-related fMRI responses for memory retrieval of voluntary actions.</b></p> <p>PLoS One. 2017;12(8):e0182657</p> <p>Authors: Lee D, Yun S, Jang C, Park HJ</p> <p>Abstract<br/> This study proposes a method for classifying event-related fMRI responses in a specialized setting of many known but few unknown stimuli presented in a rapid event-related design. Compared to block design fMRI signals, classification of the response to a single or a few stimulus trial(s) is not a trivial problem due to contamination by preceding events as well as the low signal-to-noise ratio. To overcome such problems, we proposed a single trial-based classification method of rapid event-related fMRI signals utilizing sparse multivariate Bayesian decoding of spatio-temporal fMRI responses. We applied the proposed method to classification of memory retrieval processes for two different classes of episodic memories: a voluntarily conducted experience and a passive experience induced by watching a video of others' actions. A cross-validation showed higher classification performance of the proposed method compared to that of a support vector machine or of a classifier based on the general linear model. Evaluation of classification performances for one, two, and three stimuli from the same class and a correlation analysis between classification accuracy and target stimulus positions among trials suggest that presenting two target stimuli at longer inter-stimulus intervals is optimal in the design of classification experiments to identify the target stimuli. The proposed method for decoding subject-specific memory retrieval of voluntary behavior using fMRI would be useful in forensic applications in a natural environment, where many known trials can be extracted from a simulation of everyday tasks and few target stimuli from a crime scene.<br/> </p><p>PMID: 28777830 [PubMed - in process]</p> en-us Copyright 2000-2026 NITRC OSI Sat, 02 May 2026 21:07:35 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator