Preprints

  1. Güçlütürk, Y., Güçlü, U., Seeliger, K., Bosch, S., Van Lier, R., Van Gerven, M., 2017. Deep adversarial neural decoding. arXiv. arXiv:1705.07109 [q-bio.NC]. 1-12.
  2. Ambrogioni, L., Güçlü, U., van Gerven, M.A.J., Maris, E., 2017. The kernel mixture network: A nonparametric method for conditional density estimation of continuous random variables. arXiv. arXiv:1705.07111 [stat.ML], 1–10.
  3. Ambrogioni, L., Hinne, M., van Gerven, M.A.J., Maris, E., 2017. GP CaKe: Effective brain connectivity with causal kernels. arXiv. arXiv:1705.05603 [q-bio.NC], 1–10.
  4. Güçlü, U., Güçlütürk, Y., Madadi, M., Escalera, S., Baró, X., González, J., van Lier, R., van Gerven, M.A.J., 2017. End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks ArXiv. arXiv:1703.03305 [cs.CV]. 1–18.
  5. Ambrogioni, L., Umut, G., Maris, E., van Gerven, M..A.J. 2017. Estimating nonlinear dynamics with the ConvNet smoother ArXiv. arXiv:1702.05243 [stat.ML]. 1–8.

Journal papers

  1. Hirschmann, J., Schoffelen, J. M., Schnitzler, A., & van Gerven, M. A. J. (2017). Parkinsonian rest tremor can be detected accurately based on neuronal oscillations recorded from the subthalamic nucleus. Clinical Neurophysiology. http://doi.org/10.1016/j.clinph.2017.07.419
  2. Seeliger, K., Fritsche, M., Güçlü, U., Schoenmakers, S., Schoffelen, J., Bosch, S. E., & Gerven, M. A. J. Van. (2017). Convolutional neural network-based encoding and decoding of visual object recognition in space and time. NeuroImage, doi: 10.1016/j.neuroimage.2017.07.018. http://doi.org/10.1016/j.neuroimage.2017.07.018
  3. Berezutskaya, J., Freudenburg, Z. V, Güçlü, U., van Gerven, M. A. J., & Ramsey, N. F. (2017). Neural tuning to low-level features of speech throughout the perisylvian cortex. J Neurosci, 10.1523/JNEUROSCI.0238-17.2017.
  4. Dijkstra, N., Zeidman, P., Ondobaka, S., Gerven, M. A. J. Van, and Friston, K. (2017). Distinct top-down and bottom-up brain connectivity during visual perception and imagery. Scientific Reports, 7(5677), 1–9. http://doi.org/10.1038/s41598-017-05888-8.
  5. Ambrogioni, L., Gerven, M.A.J. Van, Maris, E., 2017. Dynamic decomposition of spatiotemporal neural signals. PLoS Comp. Biol. 13(5), e1005540.
  6. Benozzo, D., Jylanki, P., Olivetti, E., Avesani, P., van Gerven, M.A.J., 2017. Bayesian estimation of directed functional coupling from brain recordings. PLoS One 12, e0177359.
  7. Dijkstra, N., Bosch, S., van Gerven, MAJ, 2017. Vividness of visual imagery depends on the neural overlap with perception in visual areas. J. Neurosci. In Press.
  8. Hinne, M, Meijers, A, Bakker, R, Tiesinga, PHE, Morup, M, van Gerven, MAJ, 2017. The missing link : Predicting connectomes from noisy and partially observed tract tracing data. PLoS Comp. Biol. 1–22. doi:10.1371/journal.pcbi.1005374
  9. Güçlü, U., Gerven, M., 2017. Modeling the dynamics of human brain activity with recurrent neural networks. J. Front. Comput. Neurosci. 11. doi:10.3389/fncom.2017.00007
  10. Janssen, RJ, Jylänki, P, van Gerven, MAJ, 2016. Let’s not waste time: Using temporal information in clustered activity estimation with spatial adjacency restrictions (CAESAR) for parcellating fMRI data. PLoS ONE, 11(12): 11(12): e0164703.
  11. Shumskaya, E, van Gerven, MAJ, Norris, DG, Vos, PE, Kessels, RPC, 2016. Abnormal connectivity in the sensorimotor network predicts attention deficits in traumatic brain injury. Experimental Brain Research. In Press.
  12. Janssen, MAM, Hinne, M, Janssen, RJ, van Gerven, MAJ, Steens, SC, Góraj, B, Koopmans, PP, Kessels, RPC, 2016. Resting-state subcortical functional connectivity in HIV-infected patients on long-term cART. Brain Imaging Behav. doi:10.1007/s11682-016-9632-4
  13. van Gerven, MAJ (2016). A primer on encoding models in sensory neuroscience. J Math Psychol. In Press.
  14. van de Nieuwenhuijzen, ME, van den Borne, E, Jensen, O, van Gerven, MAJ (2016). Spatiotemporal dynamics of cortical representations during and after stimulus presentation. Frontiers in Systems Neuroscience. DOI: 10.3389/fnsys.2016.00042.
  15. Dijkstra, N, van de Nieuwenhuijzen, ME, van Gerven, MAJ (2016). The spatiotemporal dynamics of binocular rivalry: evidence for increased top-down flow prior to a perceptual switch. Neuroscience of Consciousness. DOI: http://dx.doi.org/10.1093/nc/niw003.
  16. Jiang, H, Popov, T, Jylänki, P, Bi, K, Yao, Z, Lu, Q, Jensen, O, van Gerven, MAJ (2016). Predictability of depression severity based on posterior alpha oscillations. Clinical Neurophysiology, 127(4): 2108–2114.
  17. Güçlü, U, van Gerven, MAJ (2015). Increasingly complex representations of natural movies across the dorsal stream are shared between subjects. Neuroimage. In Press.
  18. Hinne, M, Janssen, RJ, Heskes, T, van Gerven, MAJ (2015). Bayesian estimation of conditional independence graphs improves functional connectivity estimates. PLoS Comp Biol, 11(11): e1004534.
  19. Lüttke, C, Ekman, M, van Gerven, MAJ, de Lange, FP (2015). Preference for audiovisual speech congruency in superior temporal cortex. J Cog Neurosci, 28(1), 1-7.
  20. Janssen, RJ, Jylänki, P, Kessels, RPC, van Gerven, MAJ (2015). Probabilistic model-based functional parcellation reveals a robust, fine-grained subdivision of the striatum. NeuroImage, 119, 398–405.
  21. Güçlü, U, van Gerven, MAJ (2015). Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci., 35(27):10005-10014.
  22. Jiang, H, Bahramisharif, A, van Gerven, MAJ, Jensen, O (2015). Measuring directionality between neuronal oscillations of different frequencies. Neuroimage, 118:359-367.
  23. Hinne, M, Ekman, M, Janssen, R, Heskes, T, van Gerven, MAJ (2015). Probabilistic clustering of the human connectome identifies communities and hubs. PLoS ONE, 10(1): e0117179.
  24. Jiang, H, van Gerven, MAJ, Jensen, O. Modality-specific alpha modulations facilitate long-term memory encoding in the presence of distracters. J Cogn Neurosci. 2015. 27(3):583-592
  25. Simanova, I, van Gerven, MAJ, Oostenveld, R, Hagoort, P. Predicting the semantic category of internally generated words from neuromagnetic recordings. J Cogn Neurosci. 2015. 27(1):35-45.
  26. Schoenmakers, S, Güçlü, U, van Gerven, MAJ, Heskes, T. Gaussian mixture models and semantic gating improve reconstructions from human brain activity. Frontiers in Computational Neuroscience. 2014. 8(173).
  27. Lopez-Gordo, MA, Sanchez Morillo, D, van Gerven, MAJ. Spreading codes enables the blind estimation of the hemodynamic response with short-events sequences. Int J Neur Syst. 2014. DOI: 10.1142/S012906571450035X
  28. Hinne, M, Lenkoski, A, Heskes, T, van Gerven, MAJ. Efficient sampling of Gaussian graphical models using conditional Bayes factors. Stat. 2014; DOI:10.1002/sta4.66
  29. Janssen, RJ, Hinne, M, Heskes, T, van Gerven, MAJ. Quantifying uncertainty in brain network measures using Bayesian connectomics. Frontiers in Computational Neuroscience. 2014; DOI:10.3389/fncom.2014.00126
  30. Güçlü, U and van Gerven, MAJ. Unsupervised feature learning improves prediction of human brain activity in response to natural images. PLoS Comput Biol. 2014; DOI: 10.1371/journal.pcbi.1003724.
  31. Simanova, I, Hagoort, P, Oostenveld, R, van Gerven, MAJ. Modality-independent decoding of semantic information from the human brain. Cereb Cortex. 2014; 24:426-434.
  32. Hinne M, Ambrogioni L, Janssen R, Heskes T, van Gerven, MAJ. Structurally-informed Bayesian functional connectivity analysis. NeuroImage. 2014; 86:294-305.
  33. Bahramisharif, A, van Gerven, MAJ, Aarnoutse, E, Mercier, M, Schwartz, T, Foxe, J, Ramsey, N, Jensen, O. Propagating neocortical gamma bursts are coordinated by traveling alpha waves. The Journal of Neuroscience. 2013; 33(48):18849-18854.
  34. Roijendijk, L, Farquhar, J, van Gerven, MAJ, Jensen, O, Gielen, S. Exploring the impact of target eccentricity and task difficulty on covert visual spatial attention and its implications for brain computer interfacing. PLoS ONE. 2013; 8(12):e80489.
  35. Niazi, AM, van den Broek, PLC, Klanke, S, Barth, M, Poel, M, van Gerven, MAJ. Online decoding of object-based attention using real-time fMRI. European Journal of Neuroscience. 2013; 39(2):319-329.
  36. Kok P, Brouwer GJ, van Gerven MAJ, de Lange FP. Prior expectations bias sensory representations in visual cortex. The Journal of Neuroscience. 2013; 33(41):16275-16284.
  37. van de Nieuwenhuijzen, ME, Backus, AR, Bahramisharif, A, Doeller, CF, Jensen, O, van Gerven, MAJ. MEG-based decoding of the spatiotemporal dynamics of visual category perception. Neuroimage. 2013; 83:1063-1073.
  38. Schoenmakers, S, Barth, M, Heskes, T, van Gerven, MAJ. Linear reconstruction of perceived images from human brain activity. Neuroimage. 2013; 83:951-961.
  39. Vidaurre, D, van Gerven MAJ, Bielza, C, Larrañaga, P, Heskes, T. Bayesian sparse partial least squares. Neural Computation. 2013; 25(12):3318-3339.
  40. Brouwer, A-M, Reuderink, B, Vincent, J, van Gerven, MAJ, van Erp, JBF. Distinguishing between target and nontarget fixations in a visual search task using fixation-related potentials. Journal of Vision. 2013; 13(3).
  41. Geuze, J, van Gerven, MAJ, Farquhar, J, Desain P. Detecting semantic priming at the single-trial level. PLoS ONE. 2013; 8(4): e60377
  42. van Gerven, MAJ, Maris, E, Sperling, M, Sharan, A, Litt, B, Anderson, C, Baltuch, G, Jacobs, J. Decoding the memorization of individual stimuli with direct human brain recordings. Neuroimage. 2012; 70:223-232.
  43. Hinne, M, Heskes, T, Beckmann, CF, van Gerven, MAJ. Bayesian inference of structural brain networks. Neuroimage. 2012; 66:543-552.
  44. van Gerven, MAJ, Chao ZC, Heskes, T. On the decoding of intracranial data using sparse orthonormalized partial least squares. J Neural Eng. 2012; 9(2):026017.
  45. Llera A, van Gerven MAJ, Gómez V, Kappen HJ. On the use of interaction error potentials for adaptive brain computer interfaces. Neural Networks. 2011; 24(10):1120-1127.
  46. Jensen O, Oostenveld R, Klanke S, Hadjipapas A, Okazaki Y, van Gerven MAJ. Using brain-computer interfaces and brain-state dependent stimulation as a tool in cognitive neuroscience. Frontiers in Psychology. 2011;2.
  47. van Gerven MAJ, Kok P, de Lange FP, Heskes T. Dynamic decoding of ongoing perception. Neuroimage. 2011; 57:950–957.
  48. Treder MS, Bahramisharif A, Schmidt NM, van Gerven MAJ, Blankertz B. Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention. J. NeuroEngineering and Rehabil. 2011; 8(24).
  49. Bahramisharif A, Heskes T, Jensen O, van Gerven MAJ. Lateralized responses during covert attention are modulated by target eccentricity. Neurosci. Lett. 2011; 491(1):35-39.
  50. Simanova I, van Gerven MAJ, Oostenveld R, Hagoort P. Identifying object categories from event-related EEG: toward decoding of conceptual representations. PLoS ONE. 2010; 5(12):e14465.
  51. van Gerven MAJ, de Lange FP, Heskes T. Neural decoding with hierarchical generative models. Neural Comput. 2010; 22(12):3127-3142
  52. van Gerven MAJ, Cseke B, de Lange FP, Heskes T. Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior. Neuroimage. 2010; 50(1):150-161.
  53. Bahramisharif A, van Gerven MAJ, Heskes T, Jensen O. Covert attention allows for continuous control of brain-computer interfaces. Eur. J. Neurosci. 2010; 31(8):1501-1508.
  54. van Gerven MAJ, Bahramisharif A, Heskes T, Jensen O. Selecting features for BCI control based on a covert spatial attention paradigm. Neural Netw. 2009; 22(9):1271-1277.
  55. van Gerven MAJ, Farquhar J, Schaefer R, Vlek R, Geuze J, Nijholt A, et al. The brain-computer interface cycle. J Neural Eng. 2009; 6(4):041001.
  56. van Gerven MAJ, Hesse C, Jensen O, Heskes T. Interpreting single trial data using groupwise regularisation. Neuroimage. 2009; 46(3):665-676.
  57. van Gerven MAJ, Jensen O. Attention modulations of posterior alpha as a control signal for two-dimensional brain-computer interfaces. J. Neurosci. Methods. 2009; 179(1):78-84.
  58. Willems D, Niels R, van Gerven MAJ, Vuurpijl L. Iconic and multi-stroke gesture recognition. Pattern Recognit. 2009;42(12):3303-3312.
  59. van Gerven MAJ, Taal BG, Lucas PJF. Dynamic Bayesian networks as prognostic models for clinical patient management. J Biomed Inform. 2008; 41(4):515-529.
  60. van Gerven MAJ, Lucas PJF, van der Weide, TP. A generic qualitative characterization of independence of causal influence. Internat J Approx Reas. 2008; 48(1):214-136.
  61. van Gerven MAJ, Jurgelenaite R, Taal BG, Heskes T, Lucas PJF. Predicting carcinoid heart disease with the noisy-threshold classifier. Artif Intell Med. 2007; 40(1):4555.
  62. van Gerven MAJ, Díez FJ, Taal BG, Lucas PJF. Selecting treatment strategies with dynamic limited-memory influence diagrams. Artif Intell Med. 2007; 40(3):171-186.

Selected conference and workshop proceedings

  1. Grant, E., Kohli, P, van Gerven, M.A.J. Deep disentangled representations for volumetric reconstruction ECCV 2016. Lecture Notes in Computer Science, 9915, 266-279, 2016.
  2. Güçlütürk, Y., Güçlü, U., van Lier, R., van Gerven, M.A.J. (2016). Convolutional sketch inversion. In: Hua G., Jégou H. (eds) Computer Vision – ECCV 2016 Workshops. ECCV 2016. Lecture Notes in Computer Science, vol 9913. Springer, Cham
  3. Güçlü, U., Thielen, J., Hanke, M., & van Gerven, M.A.J. Brains on beats. NIPS. 2016
  4. Güçlütürk, Y., Güçlü, U., van Gerven, M.A.J., van Lier, R., 2016. Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition. ECCV 2016. arXiv:1609.05119 [cs.CV]. 2016.
  5. Grant, E., Sahm, S., Zabihi, M, van Gerven, M.A.J.
    Predicting and visualizing psychological attributions with a deep neural network. 23rd International Conference on Pattern Recognition. 2016.
  6. Güçlü, U, Knechten, M. and van Gerven, MAJ. A two-stage approach to estimating voxel-specific encoding models improves prediction of hemodynamic responses to natural images. In: Neuroinformatics 2014. Frontiers in Neuroinformatics. 2014.
  7. Schoenmakers, S, van Gerven, MAJ, Heskes, T. Gaussian mixture models improve fMRI-based image reconstruction. In: IEEE Proceedings of Pattern Recognition in Neuroimaging. 2014
  8. van de Nieuwenhuijzen, M, Jensen, O, van Gerven, MAJ. Reading what’s on your mind: Decoding images of different categories from working memory maintenance. In: Biomag. 2014.
  9. Hinne, M, Heskes, T, van Gerven, MAJ. Structural connectivity estimation via Bayesian data fusion. In: Human Brain Mapping. 2013.
  10. Güçlü, U and van Gerven, MAJ. Unsupervised learning of invariant features for encoding fMRI responses to natural images. In: Human Brain Mapping. 2013.
  11. Hinne, M, Eckman, M, Heskes, T, van Gerven, MAJ. Learning parcellated brain networks with an infinite relational model prior. In: Bayesian Non-parametrics 9. 2013
  12. Güçlü, U and van Gerven, MAJ. Unsupervised learning of features for Bayesian decoding in functional magnetic resonance imaging. In: BENELEARN 2013.
  13. Bahramisharif, A, van Gerven, MAJ, Jensen, O. Occipital alpha power and covert visual spatial attention in 2D. In: Biomag2012.
  14. van de Nieuwenhuijzen, M, Backus, A, Bahramisharif, A, Doeller, C, Jensen, O, van Gerven, MAJ. Classifying perceived natural images from MEG data using multivariate methods. In: Biomag2012.
  15. Backus, AR, van Gerven, MAJ, Jensen, O, Doeller, C. Category-specific changes in resting-state brain connectivity on multiple timescales following a sequence encoding task. In: Amsterdam Memory Slam 2012.
  16. Backus AR, Meeuwissen EB, Jensen O, van Gerven MAJ. Investigating the spatiotemporal dynamics of long-term memory retrieval using multivariate pattern analyses on magnetoencephalography Data. In: NIPS 2011 workshop on Machine Learning and Interpretation in Neuroimaging.
  17. Wouters HJP, van Gerven MAJ, Heskes T, Treder MS, Bahramisharif A. Covert attention as a paradigm for subject-independent brain-computer interfacing. In: NIPS 2011 workshop on Machine Learning and Interpretation in Neuroimaging.
  18. Bahramisharif A, van Gerven MAJ, Schoffelen J-M, Ghahramani Z, Heskes T. The dynamic beamformer. In: NIPS 2011 workshop on Machine Learning and Interpretation in Neuroimaging.
  19. van Gerven MAJ, Maris E, Heskes T. A Markov random field approach to neural encoding and decoding. In: ICANN 2011. 2011.
  20. van Gerven MAJ, de Lange FP, Heskes T. A hierarchical generative model for percept reconstruction. In: Human Brain Mapping. 2010.
  21. van Gerven MAJ, Simanova I. Concept classification with Bayesian multi-task learning. In: NAACL-HLT workshop on Computational Neurolinguistics. 2010.
  22. van Gerven MAJ, Heskes T. Sparse orthonormalized partial least squares. In: BNAIC. 2010.
  23. Simanova I, van Gerven MAJ, Oostenveld R, Hagoort P. Identifying object categories from event-related EEG: Toward decoding of conceptual representations. In: Human Brain Mapping. 2010.
  24. van Gerven MAJ, Bahramisharif A, Jensen O. Modulations in alpha activity by covert attention: a new 2D control signal for BCI. In: 8th Dutch Endo-Neuro-Psycho Meeting. Doorwerth, the Netherlands: 2009.
  25. Bahramisharif A, van Gerven MAJ, Heskes T. Exploring the impact of alternative feature representations on BCI classification. In: European Symposium on Artificial Neural Networks. 2009.
  26. Birlutiu A, Dijkstra TMH, van Gerven MAJ, Heskes T. Does the immune system have an influence on malaria parasite gene expression? In: 5th Netherlands Institute for Systems Biology Symposium. 2009.
  27. van Gerven MAJ, Cseke B, Oostenveld R, Heskes T. Bayesian source localization with the multivariate Laplace prior. In: Bengio Y, Schuurmans D, Lafferty J, Williams CKI, Culotta A, editors. Neural Information Processing Systems 23. 2009. p. 1901-1909.
  28. van Gerven MAJ, Takashima A, Heskes T. Selecting and identifying regions of interest using groupwise regularization. In: NIPS Workshop on New Directions in Statistical Learning for Meaningful and Reproducible fMRI Analysis. 2008.
  29. van Gerven MAJ. Tensor decompositions for probabilistic classification. In: Intelligent Data Analysis in bioMedicine and Pharmacology (IDAMAP) 2007. 2007.

Technical reports

  1. Solin, A., Jylänki, P., Kauramäki, J., Heskes, T., van Gerven, M.A.J., Särkkä, S. (2016). Regularizing solutions to the MEG inverse problem using space-time separable covariance functions. arXiv:1604.04931 [stat.AP].
  2. Güçlü, U., & van Gerven, M.A.J. (2015). Semantic vector space models predict neural responses to complex visual stimuli. arXiv:1510.04738. 2015
  3. van Gerven M.A.J., Heskes T. (2008) L1/Lp regularization of differences. Radboud University Nijmegen
  4. Heskes T, van Gerven M.A.J. (2008) Stability conditions for L1/Lp regularization. Nijmegen, The Netherlands: Radboud University Nijmegen
  5. van Gerven M.A.J. (2007) Approximate inference in graphical models using tensor decompositions. Radboud University Nijmegen
  6. van Gerven M.A.J. (2006) Efficient Bayesian inference by factorizing conditional probability distributions. Nijmegen, The Netherlands: Radboud University
  7. van Gerven M.A.J, Taal BG. (2006) Structure and parameters of a Bayesian network for carcinoid prognosis. Nijmegen, The Netherlands: Radboud University Nijmegen