Linear reconstruction of perceived images from human brain activity S Schoenmakers, M Barth, T Heskes, M Van Gerven NeuroImage 83, 951-961, 2013 | 103 | 2013 |
Convolutional neural network-based encoding and decoding of visual object recognition in space and time K Seeliger, M Fritsche, U Güçlü, S Schoenmakers, JM Schoffelen, ... NeuroImage 180, 253-266, 2018 | 54 | 2018 |
Gaussian mixture models and semantic gating improve reconstructions from human brain activity S Schoenmakers, U Güçlü, M Van Gerven, T Heskes Frontiers in computational neuroscience 8, 173, 2015 | 25 | 2015 |
Cnn-based encoding and decoding of visual object recognition in space and time K Seeliger, M Fritsche, U Güçlü, S Schoenmakers, JM Schoffelen, ... BioRxiv, 118091, 2017 | 7 | 2017 |
Gaussian mixture models improve fMRI-based image reconstruction S Schoenmakers, M van Gerven, T Heskes 2014 International Workshop on Pattern Recognition in Neuroimaging, 1-4, 2014 | 3 | 2014 |
Fast quantification of uncertainty in non-linear diffusion MRI models for artifact detection and more power in group studies RL Harms, FJ Fritz, S Schoenmakers, A Roebroeck bioRxiv, 651547, 2019 | 1 | 2019 |
Brainreading: Decoding the mental state S Schoenmakers [Sl: sn], 2019 | | 2019 |
Handwritten characters in fMRI ('BRAINS'data set) S Schoenmakers, M Barth, TM Heskes, MAJ van Gerven Radboud Data Repository, 2018 | | 2018 |
Hidden Markov models for reading words from the human brain S Schoenmakers, T Heskes, M van Gerven 2015 International Workshop on Pattern Recognition in NeuroImaging, 89-92, 2015 | | 2015 |
ICA Allows Rapid Identi ication of Functional ROIs in Task-Based fMRI Data at 3T and 7T S Schoenmakers, M van Gerven | | |