Publications
2025
Taiki W. Nishihara, Fritz Gerald P. Kalaw, Adelle Engmann, Aya Motoyoshi, Paapa Mensah-Kane, Deepa Gupta, Victoria Patronilo, Linda M. Zangwill, Shahin Hallaj, Amirhossein Panahi, Garrison W. Cottrell, Bradley Voytek, Virginia R. de Sa, Sally L. Baxter.
Fostering Multidisciplinary Collaboration in Artificial Intelligence and Machine Learning Education: Tutorial Based on the AI-READI Bootcamp.
JMIR Med Educ 2025.
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Abhinav Uppal, Dillan Cellier, Min Lee, Sean Bauersfeld, Yuchen Xu, Shihab Shamma, Gert Cauwenberghs, Virginia R. de Sa.
EEG Blink Artifacts Can Identify Read Music in Listening and Imagery.
IEEE NER 2025.
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Isuru Gunasekara, Virginia R. de Sa.
Efficient Calibration in Motor Imagery BCIs Under Data Constraints via Subject Transfer.
NeurIPS Workshop 2025.
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Sriram Ravishankar, Virginia R. de Sa.
Handwriting decoding as a challenging Motor Imagery task for EEG Foundation Models.
NeurIPS Workshop 2025.
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Ian Jackson, Raunit Kohli, Eric Leonardis, Andrea A. Chiba, Virginia R. de Sa.
Explore-Exploit Behaviors During Rat-Robot Interactions Optimize Social and Spatial Security.
IEEE ICDL 2025.
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Alessandro D'Amico, Virginia R. de Sa.
Are Low-Cost EEG Systems Viable for Cognitive Neuroscience Research?.
PsyArXiv 2025.
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Abhinav Uppal, Min Suk Lee, Teng Fei, Adyant Balaji, Zhaoyi Liu, Tzyy-Ping Jung, Lara M. Rangel, Virginia R. de Sa, Rahul Parhi, Akshay Paul, Yuchen Xu, Gert Cauwenberghs.
Brain-Body Coupling in Listening to Metronomic Sounds and Music.
IEEE EMBC 2025.
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Srinivas Ravishankar, Nathan Zajzon, Virginia R. de Sa.
Decoding Imagined Handwriting from EEG.
arXiv 2025.
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Shuangquan Feng, Junhua Ma, Virginia R. de Sa.
FERGI: Automatic Scoring of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression Reaction.
IEEE FG 2025.
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Amy Eguchi, Garrison W. Cottrell, Taylor Berg-Kirkpatrick, Virginia R. de Sa.
Research Experience for Teachers in the Interdisciplinary AI – Report from the Summer AI Research Experience 2024.
2025.
2024
AI-READI Consortium.
AI-READI: rethinking AI data collection, preparation and sharing in diabetes research and beyond.
Nature Metabolism 2024.
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Christina Rizq, Alessandro D'Amico, Arya Turel, Julia Faybishenko, Min Suk Lee, Jin-Hong Kim, Gert Cauwenberghs, Virginia R. de Sa.
Development and Characterization of Zinc Dry Electrodes for Wearable Electrophysiology.
IEEE EMBC 2024.
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Shuangquan Feng, Virginia R. de Sa.
One-Frame Calibration with Siamese Network in Facial Action Unit Recognition.
arXiv:2409.00240 2024.
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Teng Fei, Srinivas Ravishankar, Zhining Chen, Abhinav Uppal, Ian Jackson, Virginia R. de Sa.
Perceptogram: Reconstructing Visual Percepts and Presumptive Electrode Preference from EEG.
arXiv 2024.
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2023
Vijay Veerabadran, Srinivas Ravishankar, Yunhao Tang, Raghav Raina, Virginia R. de Sa.
Adaptive recurrent vision performs zero-shot computation scaling to unseen difficulty levels.
NeurIPS 2023.
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Miguel J. Monares, Yuan Tang, Ritik Raina, Virginia R. de Sa.
Analyzing Biases in AU Activation Estimation Toward Fairer Facial Expression Recognition.
KDD HILDA 2023.
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2022
Ritik Raina, Miguel Monares, Mingze Xu, Xiaojing Xu, Lehan Li, William Sumerfield, Jin Gan, Virginia R. de Sa.
Exploring Biases in Facial Expression Analysis using Synthetic Faces.
NeurIPS SyntheticData 2022.
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Sarah Fabi, Xiaojing Xu, Virginia R. de Sa.
Exploring the Racial Bias in Pain Detection with a Computer Vision Model.
UCSD URC 2022.
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Zhining Chen, Mahta Mousavi, Virginia R. de Sa.
Multi-subject unsupervised transfer with weighted subspace alignment for common spatial patterns.
BCI 2022.
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Alessandro D'Amico, Virginia R. de Sa.
Set Size Effects on the P3b in a BCI Speller.
CogSci 2022.
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Mahta Mousavi, Eric Lybrand, Shuangquan Feng, Shuai Tang, Rayan Saab, Virginia R. de Sa.
Spectrally Adaptive Common Spatial Patterns.
arXiv:2202.04542 2022.
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2021
Busra Susam, Nathan Riek, Murat Akcakaya, Xiaojing Xu, Virginia R. de Sa, Hooman Nezamfar, Damaris Diaz, Kenneth Craig, Matthew Goodwin, Jeannie Huang.
Automated Pain Assessment in Children Using Electrodermal Activity and Video Data Fusion via Machine Learning.
IEEE TBME 2021.
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Vijay Veerabadran, Raghav Raina, Virginia R. de Sa.
Bio-inspired learnable divisive normalization for ANNs.
NeurIPS SVRHM 2021.
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Kuei-da Liao, Matthew V. Mollison, Tim Curran, Virginia R. de Sa.
EEG Reveals Familiarity by Controlling Confidence in Memory Retrieval.
IEEE TCDS 2021.
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Mahta Mousavi, Eric Lybrand, Shuangquan Feng, Shuai Tang, Rayan Saab, Virginia R. de Sa.
Improving Robustness in Motor Imagery Brain-Computer Interfaces.
NeurIPS DistShift 2021.
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Mahta Mousavi, Virginia R. de Sa.
Motor imagery performance from calibration to online control in EEG-based brain-computer interfaces.
IEEE NER 2021.
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Xiaojing Xu, Virginia R. de Sa.
Personalized Pain Detection in Facial Video with Uncertainty Estimation.
NeurIPS ML4H 2021.
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2020
Shuai Tang, Virginia R. de Sa.
Deep Transfer Learning with Ridge Regression.
arXiv 2020.
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Xiaojing Xu, Virginia R. de Sa.
Exploring Multidimensional Measurements for Pain Evaluation using Facial Action Units.
IEEE BigData 2020.
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Mahta Mousavi, Laurens Krol, Virginia R. de Sa.
Hybrid brain-computer interface with motor imagery and error-related brain activity.
J Neural Eng 2020.
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Vijay Veerabadran, Virginia R. de Sa.
Learning compact generalizable neural representations supporting perceptual grouping.
arXiv 2020.
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2019
Shuai Tang, Virginia R. de Sa.
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning.
ACL 2019.
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Xiaojing Xu, Jeannie S. Huang, Virginia R. de Sa.
Pain Evaluation in Video using Extended Multitask Learning from Multidimensional Measurements.
MLHC 2019.
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Mahta Mousavi, Virginia R. de Sa.
Spatio-temporal analysis of error-related brain activity in active and passive brain-computer interfaces.
J. Neural Eng. 2019.
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Mahta Mousavi, Virginia R. de Sa.
Temporally Adaptive Common Spatial Patterns with Deep Convolutional Neural Networks.
IEEE SMC 2019.
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Kara Hawley, Jeannie S. Huang, Matthew Goodwin, Damaris Diaz, Virginia R. de Sa, Kathryn A. Birnie, Christine T. Chambers, Kenneth D. Craig.
Youth and Parent Appraisals of Participation in a Study of Spontaneous and Induced Pediatric Clinical Pain.
Ethics & Behavior 2019.
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2018
Yi-Ming Jin, Mahta Mousavi, Virginia R. de Sa.
Adaptive CSP with subspace alignment for subject-to-subject transfer in motor imagery brain-computer interfaces.
IEEE BCI 2018.
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Busra T. Susam, Murat Akcakaya, Hooman Nezamfar, Damaris Diaz, Virginia R. de Sa, Kenneth D. Craig, Xiaojing Xu, Jeannie S. Huang, Matthew S. Goodwin.
Automated Pain Assessment using Electrodermal Activity Data and Machine Learning.
IEEE EMBC 2018.
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Xiaojing Xu, Kenneth D. Craig, Damaris Diaz, Matthew Goodwin, Murat Akcakaya, Busra Susam, Jeannie S. Huang, Virginia R. de Sa.
Automated Pain Detection in Facial Videos of Children using Human-Assisted Transfer Learning.
IVA 2018.
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Shuai Tang, Virginia R. de Sa.
Improving Sentence Representations with Multi-view Frameworks.
NeurIPS IRASL 2018.
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Shuai Tang, Paul Smolensky, Virginia R. de Sa.
Learning Distributed Representations of Symbolic Structure Using Binding and Unbinding Operations.
NeurIPS IRASL 2018.
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Eunho Noh, Kuei-da Liao, Matthew V. Mollison, Tim Curran, Virginia R. de Sa.
Single-trial EEG analysis predicts memory retrieval and reveals source-dependent differences.
Front. Hum. Neurosci. 2018.
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Kuei-da Liao, Matthew V. Mollison, Tim Curran, Virginia R. de Sa.
Single-Trial EEG Predicts Memory Retrieval Using Leave-One-Subject-Out Classification.
IEEE BIBM 2018.
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Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, Virginia R. de Sa.
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding.
RepL4NLP 2018.
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Laurens Krol, Mahta Mousavi, Virginia R. de Sa, Thorsten Zander.
Towards Classifier Visualization in 3D Source Space.
IEEE SMC 2018.
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2017
Daniel Maryanovsky, Mahta Mousavi, Nathanial Moreno, Virginia R. de Sa.
CSP-NN: A convolutional neural network implementation of common spatial patterns.
BCI Graz 2017.
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Ramesh Krishna Maddula, Joshua Stivers, Mahta Mousavi, Sriram Ravindran, Virginia R. de Sa.
Deep recurrent convolutional neural networks for classifying P300 BCI signals.
BCI Graz 2017.
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Shuai Tang, Hao Jin, Chen Fang, Zheng Wang, Virginia R. de Sa.
Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning.
arXiv:1710:10380) 2017.
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Mahta Mousavi, Adam Koerner, Qiong Zhang, Eunho Noh, Virginia R. de Sa.
Improving motor imagery BCI with user response to feedback.
Brain-Computer Interfaces 2017.
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Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang, Virginia R. de Sa.
Rethinking skip-thought: A neighborhood based approach.
RepL4NLP 2017.
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Joshua M. Stivers, Virginia R. de Sa.
Spelling in parallel: Towards a rapid, spatially independent BCI.
GBCIC 2017.
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Mahta Mousavi, Virginia R. de Sa.
Towards elaborated feedback for training motor imagery brain computer interfaces.
GBCIC 2017.
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Shuai Tang, Hao Jin, Chen Fang, Zheng Wang, Virginia R. de Sa.
Trimming and improving skip-thought vectors.
arXiv:1706.03148 2017.
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2014
Eunho Noh, Virginia R. de Sa.
Discriminative Dimensionality Reduction for Analyzing EEG Data.
CogSci 2014.
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David Tingley, Andrew S. Alexander, Sean Kolbu, Virginia R. de Sa, Andrea A. Chiba, Douglas A. Nitz.
Task-phase-specific dynamics of basal forebrain neuronal ensembles.
Frontiers in Systems Neuroscience 2014.
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Eunho Noh, Matthew V. Mollison, Grit Herzmann, Tim Curran, Virginia R. de Sa.
Towards a passive brain computer interface for improving memory.
BCI Graz 2014.
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Eunho Noh, Grit Herzmann, Tim Curran, Virginia R. de Sa.
Using Single-trial EEG to Predict and Analyze Subsequent Memory.
NeuroImage 2014.
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2013
Eunho Noh, Virginia R. de Sa.
Canonical Correlation Approach to Common Spatial Patterns.
IEEE NER 2013.
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Joshua M. Lewis, Laurens van der Maaten, Virginia R. de Sa.
Divvy: Fast and Intuitive Exploratory Data Analysis.
JMLR 2013.
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Priya D. Velu, Tim Mullen, Eunho Noh, Matthew C. Valdivia, Howard Poizner, Yoram Baram, Virginia R. de Sa.
Effect of visual feedback on the occipital-parietal-motor network in Parkinson's disease with freezing of gait.
Front. Neurol..
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Adam Koerner, Qiong Zhang, Virginia R. de Sa.
The Effect of Real-Time Positive and Negative Feedback on Motor Imagery Performance.
BCI Meeting 2013.
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2012
Joshua M. Lewis, Laurens van der Maaten, Virginia R. de Sa.
A Behavioral Investigation of Dimensionality Reduction.
CogSci 2012.
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Virginia R. de Sa.
An interactive control strategy is more robust to non-optimal classification boundaries.
ICMI'12 2012.
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Joshua M. Lewis, Maya Ackerman, Virginia R. de Sa.
Human Cluster Evaluation and Formal Quality Measures; A Comparative Study.
CogSci 2012.
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Joshua M. Lewis, Virginia R. de Sa.
Learning Cluster Analysis through Experience.
CogSci 2012.
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Alan E. Robinson, Virginia R. de Sa.
Spatial properties of flicker adaptation.
Vision Research.
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2011
Walter Talbott, Ian Fasel, Javier R. Molina, Virginia R. de Sa, Javier Movellan.
Coordinating Touch and Vision to Learn What Objects Look Like.
Proceedings of the 33rd Annual Conference of the Cognitive Science Society 2011.
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2010
Joshua M. Lewis, Adam S. Fouse, Virginia R. de Sa.
Cross-Modal Influence on Binocular Rivalry.
CogSci 2010.
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2008
Alan E. Robinson, Virginia R. de Sa.
Brief presentations reveal the temporal dynamics of brightness induction and White's illusion.
Vision Research 2008.
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Ayse Pinar Saygin, Jon Driver, Virginia R. de Sa.
In the footsteps of biological motion and multisensory perception: Judgements of audio-visual temporal relations are enhanced for upright walkers.
Psychological Science 2008.
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Paul S. Hammon, Scott Makeig, Howard Poizner, Emanuel Todorov, Virginia R. de Sa.
Predicting Reaching Targets from Human EEG.
IEEE SPM 2008.
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Thomas J. Sullivan, Virginia R. de Sa.
Sleeping Our Way to Weight Normalization and Stable Learning.
Neural Computation 2008.
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2007
Leo Trottier, Virginia R. de Sa.
A Multimodal Paradigm for Investigating the Perisaccadic Temporal Inversion Effect in Vision.
CogSci 2007.
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Alan E. Robinson, Paul S. Hammon, Virginia R. de Sa.
Explaining brightness illusions using spatial filtering and local response normalization.
Vision Research.
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Paul S. Hammon, Virginia R. de Sa.
Pre-processing and meta-classification for brain-computer interfaces.
IEEE TBME 2007.
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2006
Thomas J. Sullivan, Virginia R. de Sa.
A model of surround suppression through cortical feedback.
Neural Networks 2006.
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Thomas J. Sullivan, Virginia R. de Sa.
A self-organizing map with homeostatic synaptic scaling.
Neurocomputing 2006.
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Thomas J. Sullivan, Virginia R. de Sa.
Homeostatic synaptic scaling in self-organizing maps.
Neural Networks 2006.
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Paul S. Hammon, Jaime A. Pineda, Virginia R. de Sa.
Viewing motion animations during motor imagery: effects on motor imagery.
Viewing motion animations during motor 2006.
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2005
Virginia R. de Sa.
Spectral Clustering with Two Views.
ICML Workshop 2005.
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2004
Thomas J. Sullivan, Virginia R. de Sa.
A Temporal Trace and SOM-based Model of Complex Cell Development.
Neurocomputing 2004.
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Hsin-Hao Yu, Virginia R. de Sa.
Nonlinear reverse-correlation with synthesized naturalistic noise.
Neurocomputing 2004.
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Virginia R. de Sa.
Sensory Modality Segregation.
Advances in Neural Information Processing Systems 16 2004.
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2003
Cathy L. Zheng, Virginia R. de Sa, Michael Gribskov, Thidapat M. Nair.
On Selecting Features from Splice Junctions: An Analysis Using Information Theoretic and Machine Learning Approaches.
Genome Informatics 2003.
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2001
Virginia R. de Sa, David J.C. MacKay.
Model fitting as an Aid to Bridge Balancing in Neuronal Recording.
Neurocomputing 2001.
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1999
Virginia R. de Sa.
Combining Uni-Modal Classifiers to Improve Learning.
Prerational Intelligence: Adaptive Behavior and Intelligent Systems without Symbols and Logic 1999.
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Ken McRae, George S. Cree, Robyn Westmacott, Virginia R. de Sa.
Further Evidence for Feature Correlations in Semantic Memory.
Can. J. Exp. Psych. 1999.
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1998
Virginia R. de Sa, Geoffrey E. Hinton.
Cascaded Redundancy Reduction.
Network 1998.
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Virginia R. de Sa, Dana H. Ballard.
Category Learning through Multimodality Sensing.
Neural Computation 1998.
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Rich Caruana, Virginia R. de Sa.
Using Feature Selection to Find Inputs that Work Better as Outputs.
Proceedings of the 8th International Conference on Artificial Neural Networks (ICANN 98) 1998.
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Virginia R. de Sa, R. Christopher deCharms, Michael M. Merzenich.
Using Helmholtz Machines to analyze multi-channel neuronal recordings.
Advances in Neural Information Processing Systems 10 1998.
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1997
Ken McRae, Virginia R. de Sa, Mark S. Seidenberg.
On the nature and scope of featural representations of word meaning.
Journal of Experimental Psychology: General 1997.
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Virginia R. de Sa, Dana H. Ballard.
Perceptual Learning from Cross-Modal Feedback.
Psychology of Learning and Motivation 1997.
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Rich Caruana, Virginia R. de Sa.
Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs.
Advances in Neural Information Processing Systems 9 1997.
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1994
Virginia R. de Sa.
Learning Classification with Unlabeled Data.
NIPS 1994.
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Virginia R. de Sa.
Minimizing Disagreement for Self-Supervised Classification.
Proceedings of the 1993 Connectionist Models Summer School 1994.
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Virginia R. de Sa.
Unsupervised Classification Learning from Cross-Modal Environmental Structure.
PhD Dissertation.
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1993
Virginia R. de Sa, Dana H. Ballard.
A Note on Learning Vector Quantization.
Advances in Neural Information Processing Systems 5 1993.
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Ken McRae, Virginia R. de Sa, Mark S. Seidenberg.
Modeling Property Intercorrelations in Conceptual Memory.
CogSci 1993.
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Virginia R. de Sa, Dana H. Ballard.
Self-teaching through correlated input.
Computation and Neural Systems 1993.
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1992
Virginia R. de Sa, Dana H. Ballard.
Top-down teaching enables task-relevant classification with competitive learning.
IJCNN International Joint Conference on Neural Networks 1992.
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