Search Results for author: Paul Sajda

Found 11 papers, 2 papers with code

Improving Prediction of Cognitive Performance using Deep Neural Networks in Sparse Data

no code implementations28 Dec 2021 Sharath Koorathota, Arunesh Mittal, Richard P. Sloan, Paul Sajda

Cognition in midlife is an important predictor of age-related mental decline and statistical models that predict cognitive performance can be useful for predicting decline.

Bayesian recurrent state space model for rs-fMRI

no code implementations14 Nov 2020 Arunesh Mittal, Scott Linderman, John Paisley, Paul Sajda

We evaluate our method on the ADNI2 dataset by inferring latent state patterns corresponding to altered neural circuits in individuals with Mild Cognitive Impairment (MCI).

Deep Bayesian Nonparametric Factor Analysis

no code implementations9 Nov 2020 Arunesh Mittal, Paul Sajda, John Paisley

We propose a deep generative factor analysis model with beta process prior that can approximate complex non-factorial distributions over the latent codes.

Latent neural source recovery via transcoding of simultaneous EEG-fMRI

no code implementations5 Oct 2020 Xueqing Liu, Linbi Hong, Paul Sajda

Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution for inferring a latent source space of neural activity.


Unsupervised Sparse-view Backprojection via Convolutional and Spatial Transformer Networks

no code implementations1 Jun 2020 Xueqing Liu, Paul Sajda

Many imaging technologies rely on tomographic reconstruction, which requires solving a multidimensional inverse problem given a finite number of projections.

Computed Tomography (CT)

A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI

1 code implementation NeurIPS 2019 Tao Tu, John Paisley, Stefan Haufe, Paul Sajda

In this study, we develop a linear state-space model to infer the effective connectivity in a distributed brain network based on simultaneously recorded EEG and fMRI data.


Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials

1 code implementation12 Mar 2018 Nicholas R. Waytowich, Vernon Lawhern, Javier O. Garcia, Jennifer Cummings, Josef Faller, Paul Sajda, Jean M. Vettel

Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli.

EEG General Classification

Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest

no code implementations14 Sep 2017 Victor Shih, David C Jangraw, Paul Sajda, Sameer Saproo

However, Human-AI interaction for such AI agents should include additional reinforcement that is implicit and subjective -- e. g. human preferences for certain AI behavior -- in order to adapt the AI behavior to idiosyncratic human preferences.


A comparison of single-trial EEG classification and EEG-informed fMRI across three MR compatible EEG recording systems

no code implementations25 Jul 2017 Josef Faller, Linbi Hong, Jennifer Cummings, Paul Sajda

Simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can be used to non-invasively measure the spatiotemporal dynamics of the human brain.

Classification EEG +1

Fast Simultaneous Training of Generalized Linear Models (FaSTGLZ)

no code implementations31 Jul 2013 Bryan R. Conroy, Jennifer M. Walz, Brian Cheung, Paul Sajda

We present an efficient algorithm for simultaneously training sparse generalized linear models across many related problems, which may arise from bootstrapping, cross-validation and nonparametric permutation testing.

General Classification

Second Order Bilinear Discriminant Analysis for single trial EEG analysis

no code implementations NeurIPS 2007 Christoforos Christoforou, Paul Sajda, Lucas C. Parra

Traditional analysis methods for single-trial classification of electro-encephalography (EEG) focus on two types of paradigms: phase locked methods, in which the amplitude of the signal is used as the feature for classification, i. e. event related potentials; and second order methods, in which the feature of interest is the power of the signal, i. e event related (de)synchronization.

Classification EEG +2

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