Published in NeuroImage, 2020
This paper is about removing motion artifacts in fNIRS data via denoising autoencoder.
Recommended citation: Gao, Y., Chao, H., Cavuoto, L., Yan, P., Kruger, U., Norfleet, J., Makled, B., Schwaitzberg, S., De, S., Intes, X. (2020). "Deep Learning-based Motion Artifact Removal in Functional Near-Infrared Spectroscopy (fNIRS)" Neuroimage. Under review.
Published in Frontiers in Neuroscience, 2020
This paper is review paper of experimental neuroimaging studies of the effect of transcranial electrical stimulation on human motor skills.
Recommended citation: Gao, Y., Cavuoto, L., Schwaitzberg, S., Norfleet, J., Intes, X., De, S. (2020). "The effects of transcranial electrical stimulation on human motor functions: A comprehensive review of functional neuroimaging studies" Frontiers in Neuroscience. 14:744 (2020). DOI: https://doi.org/10.3389/fnins.2020.00744.
Published in IEEE TBME, 2020
This paper is about predicting surgical skill by fNIRS data via deep learning.
Recommended citation: Gao, Y., Yan, P., Kruger, U.,Cavuoto, L., Schwaitzberg, S., De, S., Intes, X. (2020). "Functional brain imaging reliably predicts bimanual motor skill performance in a standardized surgical task" IEEE TBME. Early Access. DOI: 10.1109/TBME.2020.3014299.
Published in Surgery, 2019
This paper is about the learning curve prediction.
Recommended citation: Gao, Y., Kruger, U., Schwaitzberg, S., Intes, X., De, S. (2019). "A Machine Learning approach to predict surgical learning curves." Surgery. 167(2), 321-327.DOI: https://doi.org/10.1016/j.surg.2019.10.008. https://www.sciencedirect.com/science/article/abs/pii/S0039606019307299