Hidden process models

We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling hidden cognitive processes in the brain, given functional Magnetic Resonance Imaging (fMRI) data. fMRI data is sparse, high-dimensional, non-Markovian, and often involves prior knowledge of the form "hidden event A occurs n times within the interval [t,t′]." HPMs provide a generalization of the widely used General Linear Model approaches to fMRI analysis, and HPMs can also be viewed as a subclass of Dynamic Bayes Networks.

• Publication year

  2006

• Venue

  International Conference on Machine Learning

• Publication date

  2006-06-25

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1145/1143844.1143899
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Planning and control

  2019cited by this paper
• Identifying natural images from human brain activity

  2008cited by this paper
• Predicting Human Brain Activity Associated with the Meanings of Nouns

  2008influential reference
• A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

  2008cited by this paper
• Convex multi-task feature learning

  2008cited by this paper
• Modeling state-related fMRI activity using change-point theory

  2007cited by this paper
• Hidden Markov multiple event sequence models: A paradigm for the spatio-temporal analysis of fMRI data

  2007cited by this paper
• Classification in Very High Dimensional Problems with Handfuls of Examples

  2007cited by this paper
• Bayesian Network Learning with Parameter Constraints

  2006cited by this paper
• Neuroimaging: Decoding mental states from brain activity in humans

  2006cited by this paper
• Decoding the visual and subjective contents of the human brain

  2005cited by this paper
• Modeling Neuronal Interactivity using Dynamic Bayesian Networks

  2005cited by this paper
• Exploiting parameter domain knowledge for learning in bayesian networks

  2005cited by this paper
• Exploiting Temporal Information in Functional Magnetic Resonance Imaging Brain Data

  2005cited by this paper
• Convex Optimization

  2004cited by this paper
• An integrated theory of the mind.

  2004cited by this paper
• Learning to Decode Cognitive States from Brain Images

  2004cited by this paper
• Information Theory, Inference, and Learning Algorithms

  2004cited by this paper
• All of Statistics: A Concise Course in Statistical Inference

  2004cited by this paper
• Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex

  2003cited by this paper
• Hierarchical Topic Models and the Nested Chinese Restaurant Process

  2003cited by this paper
• Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment

  2003cited by this paper
• A signal subspace approach for modeling the hemodynamic response function in fMRI.

  2003cited by this paper
• Tracking cognitive processes with functional MRI mental chronometry.

  2003cited by this paper
• Dynamic Bayesian Networks

  2002cited by this paper
• Estimating the Delay of the fMRI Response

  2002cited by this paper
• Principal Component Analysis

  2002cited by this paper
• Detecting Latency Differences in Event-Related BOLD Responses: Application to Words versus Nonwords and Initial versus Repeated Face Presentations

  2002cited by this paper
• Bayesian Methods for Hidden Markov Models

  2002cited by this paper
• Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex

  2001cited by this paper
• Spatial Heterogeneity of the Nonlinear Dynamics in the FMRI BOLD Response

  2001cited by this paper
• Reading Span and the Time-course of Cortical Activation in Sentence-Picture Verification

  2001cited by this paper
• A Bayesian Time-Course Model for Functional Magnetic Resonance Imaging Data: Rejoinder

  2000cited by this paper
• Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics.

  2000cited by this paper
• A Bayesian Time-Course Model for Functional Magnetic Resonance Imaging Data

  2000cited by this paper
• The Challenge of Functional Magnetic Resonance Imaging

  1999cited by this paper
• Optimal experimental design for event‐related fMRI

  1999influential reference
• Bayesian Modelling of fMRI lime Series

  1999cited by this paper
• An Introduction to Variational Methods for Graphical Models

  1999cited by this paper
• Computational modeling of high‐level cognition and brain function

  1999cited by this paper
• Mental chronometry using latency-resolved functional MRI.

  1998cited by this paper
• Introduction to Monte Carlo Methods

  1998cited by this paper
• Learning Dynamic Bayesian Networks

  1997cited by this paper
• Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1

  1996influential reference
• MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES

  1996cited by this paper
• Neural networks for pattern recognition

  1995cited by this paper
• Dynamic Bayesian networks

  1990cited by this paper
• A tutorial on hidden Markov models and selected applications in speech recognition

  1989cited by this paper
• Probabilistic Temporal Reasoning

  1988cited by this paper
• Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

  1977cited by this paper
• Human Brain Mapping 5:329–340(1997) � Selective Averaging of Rapidly Presented Individual Trials Using fMRI

  year unknowncited by this paper

• Enhancing ERP component detection by estimating ERP latency variability using hidden process model

  2020cites this paper
• Population based spatio-temporal probabilistic modelling of fMRI data

  2018cites this paper
• Tracking Story Reading in the Brain

  2012cites this paper
• Thought Recognition: Predicting and Decoding Brain Activity Using the Zero-Shot Learning Model

  2011cites this paper
• Machine learning classifiers and fMRI: A tutorial overview

  2009cites this paper
• Modeling fMRI data generated by overlapping cognitive processes with unknown onsets using Hidden Process Models

  2009cites this paper
• Predicting the BOLD Response: A Computational Model of Humans Solving an Arithmetic Task

  2009cites this paper
• A Combined Expression-Interaction Model for Inferring the Temporal Activity of Transcription Factors

  2008influential citation
• Classification in Very High Dimensional Problems with Handfuls of Examples

  2007cites this paper
• Continuous hidden process model for time series expression experiments

  2007cites this paper
• Modeling the fMRI Signal via Hierarchical Clustered Hidden Process Models

  2007cites this paper
• Hidden Process Models : Decoding Overlapping Cognitive States with Unknown Timing

  2006cites this paper
• Bayesian Network Learning with Parameter Constraints

  2006influential citation