Friday, April 05, 2013

ICANNGA'13 - Tom Heskes: Reading the Brain with Bayesian Machine Learning

The International Conference on Adaptive and Natural Computing algorithms, ICANNGA'13 opened with a keynote talk by prof. Tom Heskes from the Radboud University of Nijmegen titled Reading the Brain with Bayesian Machine Learning.

Technology Review had listed Bayesian Machine Learning (BML) as one of the emerging technologies that will change our world in 2004, and we were first treated with a short introduction to BML, and the current state of the research. There are many general purpose software tools such as Bugs, Jags and available and quite a few cool models, but killer applications and better techniques for discovering causal relations are still needed.

Heskes then gave examples of applications of Bayesian ML in the neuroimaging domain, or more specifically those related to Brain-Computer Interfaces. He defined the goal as classifying the mental states of the brain. 'The holy grail' of the research would be to provide the means of communication for a person who has lost all motor control (for example due to ALS).

That goal has not been reached yet, but we were given some examples of the state of current research. First Heskes showed us how it is possible to classify imagined movement of fingers from EEG data. In another experiment the focus of covert attention (focus attention without moving your eyes) on different directions was used instead. The results are strong enough to predict the angle the person was attending.

Functional Magnetic Resonance Imaging (fMRI) provides a possibility to try to classify image data. First a goal was to classify handwritten 6's versus 9's based on the fMRI data. The next logical step is then to try to predict what image the subject was seeing, and further down the road predict what a person is imagining. Currently, it was possible to do reconstruction of the handwritten 6's and 9'swith Deep Bolzmann machines (with a background idea that the brain might be doing something similar).

If this isn't enough, the Bayesian framework has also been used for inferring brain networks yielding a clustered graph. All in all, a very enlightening talk and a great start for the conference.

(Picture courtesy of Tom Heskes)

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