Lars Kai Hansen

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Researcher (help)
Lars Kai Hansen
Affiliation: DTU Compute
Center for Integrated Molecular Brain Imaging
Location: Denmark
Position: Professor
Interest(s): Machine learning
Neuroinformatics
Databases: Brede Database Google Scholar Microsoft Academic Search Scopus Twitter (Tweetteresearch) VideoLectures
Link(s): http://www2.imm.dtu.dk/~lkh/
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Lars Kai Hansen is Professor at the Technical University of Denmark heading the Section for Cognitive Systems at DTU Informatics.

Lars Kai Hansen has been involved in development of analysis methods for neuroimaging data, e.g., he did early work on prediction models for voxel-based data[1], independent component analysis and other multivariate methods.[2] Among his students has been Bartłomiej Wilkowski, Finn Årup Nielsen, Morten Mørup and Sune Lehmann Jørgensen. Lars Kai Hansen has had a long time collaboration with Stephen Strother.

[edit] Papers

  1. Consensus inference in neuroimaging
  2. Exploring fMRI data for periodic signal components
  3. Generalizable patterns in neuroimaging: how many principal components?
  4. Good friends, bad news - affect and virality in Twitter
  5. Mobile real-time EEG imaging Bayesian inference with sparse, temporally smooth source priors
  6. Multivariate strategies in functional magnetic resonance imaging
  7. Neural network ensembles
  8. What to measure next to improve decision making? On top-down task driven feature saliency

[edit] As co-author

  1. A cure for variance inflation in high dimensional kernel principal component analysis
  2. A smartphone interface for a wireless EEG headset with real-time 3D reconstruction
  3. Algorithms for sparse nonnegative Tucker decompositions
  4. Bayesian inference for structured spike and slab priors
  5. Bayesian model comparison in nonlinear BOLD fMRI hemodynamics
  6. Biclique communities
  7. Bridging the gap between coordinate- and keyword-based search of neuroscientific databases by UMLS-assisted semantic keyword extraction
  8. Cluster analysis of activity-time series in motor learning
  9. Coordinate-based meta-analytic search for the SPM neuroimaging pipeline: the BredeQuery plugin for SPM5
  10. Detecting hierarchical structure in networks
  11. EEG sequence imaging: a Markov prior for the variational garrote
  12. Effect of spatial alignment transformations in PCA and ICA of functional neuroimages
  13. ERPWAVELAB a toolbox for multi-channel analysis of time-frequency transformed event related potentials
  14. FindZebra: a search engine for rare diseases
  15. Frontal alpha oscillations distinguish leaders from followers: multivariate decoding of mutually interacting brains
  16. Input space regularization stabilizes pre-images for kernel PCA de-noising
  17. Interactive information visualization in neuroimaging
  18. Introduction to the issue on fMRI analysis for human brain mapping
  19. Large scale topic modeling made practical
  20. Model order estimation for independent component analysis of epoched EEG signals
  21. Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG
  22. Model selection for Gaussian kernel PCA denoising
  23. Model sparsity and brain pattern interpretation of classication models in neuroimaging
  24. Modeling the haemodynamic response in fMRI using smooth FIR filters
  25. Motion correction of single-voxel spectroscopy by independent component analysis applied to spectra from nonanesthetized pediatric subjects
  26. On clustering fMRI time series
  27. Plurality and resemblance in fMRI data analysis
  28. Privacy for personal neuroinformatics
  29. Proprioceptive evoked gamma oscillations
  30. Real-time monitoring of sentiment in business related Wikipedia
  31. Regularity increases middle latency evoked and late induced beta brain response following proprioceptive stimulation
  32. Regularized pre-image estimation for kernel PCA de-noising: input space regularization and sparse reconstruction
  33. Segmentation of age-related white matter changes in a clinical multi-center study
  34. Smartphones get emotional: mind reading images and reconstructing the neural sources
  35. Sparse but emotional decomposition of lyrics
  36. Sparse non-linear denoising: generalization performance and pattern reproducibility in functional MRI
  37. Specialized tools are needed when searching the web for rare disease diagnoses
  38. Shift-invariant multilinear decomposition of neuroimaging data
  39. Testing for difference between two groups of functional neuroimaging experiments
  40. The quantitative evaluation of functional neuroimaging experiments: mutual information learning curves
  41. Theorems on positive data: on the uniqueness of NMF

[edit] References

  1. Lars Kai Hansen, Benny Lautrup, Ian Law, Niels Mørch, Jens Thomsen, (1994 August) Extremely Ill-posed Learning. http://isp.imm.dtu.dk/publications/articles_1994.html
  2. Lars Kai Hansen (2007). "Multivariate strategies in functional magnetic resonance imaging". Brain and Language 102(2): 186-191. doi: 10.1016/j.bandl.2006.12.004. PMID: 17223190.
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