no code implementations • 15 Sep 2023 • Xiaoxiao Sun, Paul Sajda
In summary, our proposed analysis framework overcomes the limitations of existing polar coordinate-based clustering methods and provides a more accurate and efficient way to cluster circular data.
no code implementations • 26 Aug 2023 • Sharath Koorathota, Nikolas Papadopoulos, Jia Li Ma, Shruti Kumar, Xiaoxiao Sun, Arunesh Mittal, Patrick Adelman, Paul Sajda
We find that the ViT performance is improved in accuracy and number of training epochs when using JSF and FAX.
no code implementations • 14 Mar 2023 • Arunesh Mittal, Kai Yang, Paul Sajda, John Paisley
Several approximate inference methods have been proposed for deep discrete latent variable models.
no code implementations • 27 Nov 2022 • Xueqing Liu, Tao Tu, Paul Sajda
Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution.
no code implementations • 28 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.
no code implementations • 14 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).
no code implementations • 9 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.
no code implementations • 5 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.
no code implementations • 1 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.
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.
1 code implementation • 12 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.
no code implementations • 14 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.
no code implementations • 25 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.
no code implementations • 31 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.
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.