Human decision making is complex and influenced by many factors on multiple time scales, reflected in the numerous brain networks and connectivity patterns involved as revealed by fMRI. We address mislabeling issues in paradigms involving complex cognition, by considering a manifold regularizing prior for modeling a sequence of neural events leading to a decision. The method is directly applicable for online learning in the context of real-time fMRI, and our experimental results show that the method can efficiently avoid model degeneracy caused by mislabeling.
|Title of host publication||Machine Learning and Interpretation in Neuroimaging : International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions|
|Publication status||Published - 2012|
|Event||International Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2011) - Granada, Spain|
Duration: 16 Dec 2011 → 17 Dec 2011
|Workshop||International Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2011)|
|Period||16/12/2011 → 17/12/2011|
|Series||Lecture Notes in Computer Science|