The research uses a method called graphical models that has been widely applied to problems in computer science like speech-to-text or automated video analysis. The graphical models used by the MIT team are diagrams composed of circles and arrows that represent how neural activity results from a person's intentions for the prosthetic device they are using.
The diagrams represent the mathematical relationship between the person's intentions and the neural manifestation of that intention, whether the intention is measured by an electoencephalography (EEG), intracranial electrode arrays or optical imaging. These signals could come from a number of brain regions, including cortical or subcortical structures.
Until now, researchers working on brain prosthetics have used different algorithms depending on what method they were using to measure brain activity. The new model is applicable no matter what measurement technique is used, according to Srinivasan. We don't need to reinvent a new paradigm for each modality or brain region, he said.
Srinivasan is quick to underscore that many challenges remain in designing neural prosthetic algorithms before they are available for people to use. While the algorithm is unifying, it is not universal: the algorithm consolidates multiple avenues of development in prostheses, but it isn't the final and only approach these researchers expect to see in the years to come. Moreover, energy efficiency and robustness are key considerations for any portable, implantible bio-electronic device.
Through a better quantitative understanding of how the brain normally controls movement and the mechanisms of disease, he hopes these devices could one day allow for a level of dexterity depicted in movies, such as actor Will Smith's mechanical arm in the movie I R
|Contact: Elizabeth Thomson|
Massachusetts Institute of Technology