Microsoft has recently launched the "Cortana Intelligence Competitions" to promote their machine learning cloud service, Azure ML. The intiative is akin to Kaggle in the sense that they will be hosting online machine learning and data science competitions for enthusiasts to parcitipate in. The first competition went live a couple of weeks ago and is titled Decoding Brain Signals. Below is the description:

"The link between object perception and brain activity in visual cortical areas is a problem of fundamental importance in neuroscience. This competition asks you to build machine learning models in Microsoft Cortana Intelligence Suite to decode perceptions of human subjects from brain, specifically Electrocorticographic (ECoG) signals. The learning model needs to predict whether the human subject is seeing a house image (stimulus class 1) or a face image (stimulus class 2) from the ECoG signals collected from the subtemporal cortical surfaces of four seizure patients.

300 gray-scale images of houses (labeled as image class 1) or faces (labeled as image class 2), were displayed in a random order on a screen to the patients. ECoG signals were collected from the cortical surfaces of these patients during the experiments. The competition is to decode visual perceptions of these subjects from the ECoG signals, to predict whether the patient is seeing a house image (class 1) or a face image (class 2).

Each image stimulus is displayed to a patient for exactly 400 milliseconds, followed by a 400-millisecond inter-stimulus interval (ISI) where a blank image is displayed. A stimulus presentation cycle consists of the 400-millisecond ISI, followed by the 400-ms image stimulus. The ECoG signals were collected at the frequency of 1000 per second, i.e., every 1 millisecond there was a signal sample. Each patient has exactly 300 stimulus presentation cycles. In this competition, we share the ECoG signals of the first 200 stimulus presentation cycles and their stimulus types (1-50 are different house stimulus (stimulus class 1 in this binary classification task), and 51-100 are different face stimulus (stimulus class 2 in this binary classification task)).

Similar work on another set of 7 patients has been published at PLOS Computational Biology. The data we used in this competition was provided by the author of this paper, Dr. Kai J. Miller. This data was collected from 4 patients in the same experiment as the 7 patients in his paper. These 4 patients do not overlap with the 7 patients. However, reading through Dr. Miller’s paper may be helpful for you to understand the experimental settings, the ECoG signals, and features that were created in the machine learning experiment."

I think this will be fun for those of us interested in machine learning and data science. Plus, we get some experience with the Azure ML Suite which can look good on a CV. If you want more information you can check out the website.