Analysis of brain and genetic data

Modern experimental neuro-science has been revolutionarized by sophisticated measurement equipment, such as fMRI, MEG and others. Also, the advances in EEG measurement systems has accelerated the research on Brain Computer Interfaces. Thirdly, research tools in genetics have led to an explosion of DNA and expression data. These massive data sets require advanced data analysis tools. Machine learning methods (kernel methods, sparse dimension reduction methods, ICA, Bayesian approaches) provide the most promising approach to analyze these data.

We are engaged in collaboration with Human Genetics on the genetic origin of disorders. In the past, we have applied an advanced approximate inference method (the Cluster Variation Method) to construct haplotypes in complex pedigrees. The software is publicly available. Aladin is a software tool for performing efficient linkage analysis of a small number of distantly-related individuals. It estimates multipoint IBD probabilities and parametric LOD scores. We currently analyze data from genome-wide association studies using a Bayesian Gaussian process regression approach (with Prof. Han Brunner, and Prof. Barbara Franke).

We are engaged in collaboration with Human Genetics on the analysis of fMRI images and their genetic correlates in collaboration with Prof. Jan Buitelaar and Prof. Christian Beckmann.

Brain Computer Interface

Since 2009, we have started research on the design of an adaptive BCI system, based on the idea that subjects will be surprised when the BCI output differs from their expectation. This surprise is measurable as a so-called error potential. The detection of the error potential can be used to adapt the BCI device, using Bayesian inference.

Related Articles

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Path integral control and state dependent feedback.
Physical Review E, vol. 91, no. 032104, pp. 1-6, 2015

file type image Kappen H.J., Gómez V.
The variational garrote.
Machine Learning Journal, vol. 96, no. 3, pp. 269-294, 2014

file type image Llera A., Gómez V., Kappen H.J.
Adaptive multi class classification on bci.
Neural Computation, pp. 1-20, 2014

Llera A., Gómez V., Kappen H.J.
Quantitative analysis of task selection for brain–computer interfaces..
Journal of Neural Engineering, vol. 11, no. 5, pp. 056002, 2014

file type image Wiegerinck W.A.J.J., Mirchev M., Burgers W.G., Selten F.M.
Supermodeling dynamics and learning mechanisms.
Consensus and Synchronization in Complex Networks,, pp. 227-255, 2013

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