Machine Learning Moves the Needle on Neural Science

Machine Learning Moves the Needle on Neural Science

Machine Learning Moves the Needle on Neural Science
This post is by Chirag Dhull, Product Marketing and Hang Zhang, Senior Data Science Manager, at Microsoft.

Millions of people suffer from brain-related injuries and disorders every year. Being able to decode human perceptions from brain signals can benefit this population greatly. That’s what inspired Stanford University neurosurgeon Dr. Kai Miller to team up with Microsoft to offer the inaugural Cortana Intelligence Competition: Decoding Brain Signals. 

“The brain is an electrical organ with over 100 trillion synapses, connecting more than 87 billion neurons,” explains Dr. Miller. “Neuroscientists and computer scientists have been working to understand the secrets hidden in brain signals streaming directly from brains. When we can read and decode brain signals, we can help treat, heal, and retrain a brain after injury.”

The Decoding Brain Signals competition allowed machine learning experts and data scientists from around the world to test their skills while helping further the cause of neuroscience research. The contest asked participants to build intelligent models to decode electrical brain signals that were gathered from Dr. Miller’s research with epilepsy patients.

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“I’ve worked with a lot of patients who have electrodes implanted in their brain for seizure detection,” explains Dr. Miller. “We did a series of experiments where we showed the patients pictures of faces and houses. Those two types of pictures produce electric activity in different brain areas. The purpose of the competition was to see if people could come up with inventive new algorithms that would allow us to decode what the patient had seen and give us new intuition into the underlying nature of these signals.”

More than 600 data scientists in over 170 countries responded to the challenge. They submitted over 1,800 solutions to tackle this problem. In the end, the three top winners managed to find solutions that were 10% more accurate than Dr. Miller’s solution.

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