This paper presents a review of current brain-machine interface research with respect to how such BMI devices would be used for neuroprosthetic devices. The authors present a high level overview of the current research at each step for a neuroprothestic device to be realized. There are currently 4 main techniques for reading data from the brain: single-unit sensors, local field potentials, ECoG, and EEG. Single-unit sensors consist of tiny sensors places directly next to the neurons they are reading. Local field potentials consist of a small array of sensors located within a micro region of interest and can pick up signals from multiple neurons. It uses the local field potentials of the nearby neurons to determine if any of them are firing. ECoG is embedded into the skull or directly on the outer cranium and picks up electrical fields over much larger areas than the other two techniques. EEG is usually non-invasive and is the least accurate as far as detection of single neuron firing. Each method has its pros and cons usually with relation to invasiveness versus signal precision.
After signals are processed, they need to be interpreted and turned into actions. The ability to decode actionable intentions from the sensors is an area which requires more work. After identifying and translating thoughts into actions, it is important that neuroprosthetic devices contain feedback mechanisms. The authors discuss a handful of different feedback techniques all of which are still actively being researched. There is no best method for providing feedback and any quality BMI neuroprosthetic device would likely contain multiple feedback methods.
This paper is 2 years old and I’m curious how much the state of the art has changed since its publication. I’ve read of what seem like a few advances in the areas presented by this review but nothing conclusive to say a key problem has been solved. For instance a recent DARPA funded neuroprosthetic device in humans is already in trials. Also, recent theoretical work to understanding the Connectome is still many years away but the authors seemed to brush over the concept of understanding the complexity of the neural connections themselves in order to properly identify neurons of interest.
This paper was a review paper and was pretty much focused on presenting the future work to produce neuroprothestic devices. Almost everything in this paper could be considered future work. One area that particularly interests me is the need for more research to understand various feedback mechanisms. Current work with feedback other than visual feedback is limited.
Another area of work that seems interesting is the need to develop learning algorithms that can improve operation with feedback from the devices as well as adaption to the subject’s neurons. Adaptive learning algorithms must be developed which are robust enough to work in dynamic environment with neural connections having the ability to rewire themselves. My prior experience with genetic algorithms may provide a possible research transition into neuroscience with respect to signal processing, feedback interpretation, and actionable intent identification using genetic algorithms as a part of the learning algorithm.
Conclusions, Interests and Reflections:
This was a great paper for me because I lack much depth in the field still and it provided me with a really good overview of the state of the art in neuroprothestics. I was conducting my own review of the field so this was a great crash course on BMI neuroprosthetic devices, albeit, a high level overview. It put things in perspective and outlined several potential future research paths that I may follow.
Parag G. Patil, Dennis A. Turner – The Development of Brain-Machine Interface Neuroprosthetic Devices – Neurotherapeutics – January 2008 (Vol. 5, Issue 1, Pages 137-146, DOI: 10.1016/j.nurt.2007.11.002)