Decoding Hidden Cognitive States From Behavior and Physiology Using a Bayesian Approach

DOI10.1162/neco_a_01196zbMath1429.92051DBLPjournals/neco/YousefiBPPEDCWE19OpenAlexW2964164757WikidataQ92118195 ScholiaQ92118195MaRDI QIDQ5214369

Ishita Basu, Emad N. Eskandar, Angelique C. Paulk, Alik S. Widge, Darin D. Dougherty, Sydney S. Cash, Uri T. Eden, Ali Yousefi, Noam Peled

Publication date: 7 February 2020

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_01196

zbMATH Keywords

Bayesian approachhidden cognitive statesneural decoder

Mathematics Subject Classification ID

Neural biology (92C20) Cognitive psychology (91E10)

Uses Software

• R
• S-PLUS
• glmnet
• FieldTrip
• Psychophysics Toolbox
• VideoToolbox
• GitHub
• ieil
• PRMLT

Cites Work

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• The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis
• Learning to decode cognitive states from brain images
• Bayesian decoding of neural spike trains
• A mixed filter algorithm for cognitive state estimation from simultaneously recorded continuous and binary measures of performance
• Linear structural equations with latent variables
• Robustness of neuroprosthetic decoding algorithms
• A Spike-Train Probability Model
• Backward Simulation Methods for Monte Carlo Statistical Inference
• Model Selection and Model Averaging
• Latent Variable Modelling: A Survey*
• A group bridge approach for variable selection
• Probability Inequalities for Sums of Bounded Random Variables
• Handbook of stochastic methods for physics, chemistry and the natural sciences.

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