Mind-Machine Interfacing: Neural Decoding by Particle Filtering

Anthony Brockwell

Abstract

Over the last decade, neuroscientists have made significant breakthroughs in understanding the relationship between individual firing patterns of neurons in the motor cortex, and arm/hand motion. As a result, it is now possible, given measurements of neural activity, to "decode" the hand position, that is, to compute real-time approximations of actual hand position. This could potentially lead to the development of a new generation of robotic prosthetic devices for people who have lost limbs. I will discuss the existing technology, present real data measured in a monkey's motor cortex, and argue that significant improvements in decoding quality can be obtained by (1) building a formal statistical model, and (2) applying a recently-developed technique known as "particle filtering".

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