Researchers from MIT created an algorithmic framework for building prosthetic devices that are controlled by neural signal detectors such as EEG.
Neural prosthetic devices represent an engineer’s approach to treating paralysis and amputation. Here, electronics are used to monitor the neural signals that reflect an individual’s intentions for the prosthesis or computer they are trying to use. Algorithms form the link between neural signals that are recorded, and the user’s intentions that are decoded to drive the prosthetic device.
Over the past decade, efforts at prototyping these devices have divided along various boundaries related to brain regions, recording modalities, and applications. The MIT technique provides a common framework that underlies all these various efforts.
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.
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Paper: General Purpose Filter Design for Neural Prosthetic Devices J Neurophysiol (May 23, 2007). doi:10.1152/jn.01118.2006