Predicting visual attention using gamma kernels. - Ryan Burt; Eder Santana; José C. Príncipe; Nina Thigpen; Andreas Keil (ICASSP, 2016)

Pre-target alpha power predicts the speed of cued target discrimination. - Nathan M. Petro; Nina Thigpen; Steven Garcia; Maeve R. Boylan; Andreas Keil (NeuroImage, 2019)

Unsupervised Foveal Vision Neural Networks with Top-Down Attention. - Ryan Burt; Nina N. Thigpen; Andreas Keil; José C. Príncipe (CoRR, 2020)

Representation learning for improved interpretability and classification accuracy of clinical factors from EEG. - Garrett Honke; Irina Higgins; Nina Thigpen; Vladimir Miskovic; Katie Link; Pramod Gupta; Julia Klawohn; Greg Hajcak (CoRR, 2020)

Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data. - Ayse S. Cakmak; Nina Thigpen; Garrett Honke; Erick Andres Perez-Alday; Ali Bahrami Rad; Rebecca Adaimi; Chia-Jung Chang; Qiao Li 011; Pramod Gupta; Thomas Neylan; Samuel A. McLean; Gari D. Clifford (CoRR, 2020)

Representation learning for improved interpretability and classification accuracy of clinical factors from EEG. - Garrett Honke; Irina Higgins; Nina Thigpen; Vladimir Miskovic; Katie Link; Sunny Duan; Pramod Gupta; Julia Klawohn; Greg Hajcak (ICLR, 2021)

Unsupervised foveal vision neural architecture with top-down attention. - Ryan Burt; Nina N. Thigpen; Andreas Keil; José C. Príncipe (Neural Networks, 2021)