Virginia de Sa

Associate Professor

We use computational modeling, psychophysics studies, and machine learning to learn more about visual and multi-sensory perception.

Velu, P.D, Mullen, T., Noh, E., Valdivia, M.C, Poizner, H., Baram, Y. & de Sa, V.R. (2014). Effect of visual feedback on the occipital-parietal-motor network in Parkinson's disease with freezing of gait. (to appear in Frontiers in Movement Disorders).

Velu, P. D. & de Sa, V.R. (2013). Single-trial classification of gait and point movement preparation from human EEG. Frontiers in Neuroprosthetics 7(84)

Robinson, A. E. & de Sa, V.R. (2013). Dynamic brightness induction causes flicker adaptation, but only along the edges: evidence against the neural filling-in of brightness. Journal of Vision 13(6)17, 1-14

Noh, E., Herzmann, G., Curran, T. & de Sa, V.R. (2014). Using Single-trial EEG to Predict and Analyze Subsequent Memory. Neuroimage 84(1):712-723.

Lewis, J.M, Van der Maaten, L., & de Sa, V.R. (2013). Divvy: Fast and Intuitive Exploratory Data Analysis. Journal of Machine Learning Research 14(Oct):3159-3163.

Noh, E. & de Sa, V.R. (2013). Canonical Correlation Approach to Common Spatial Patterns. In Proceedings of the 6th International IEEE EMBS Neural Engineering Conference. Nov 6-8, 2013, San Diego, CA.