Title :
Information Systems Opportunities in Brain–Machine Interface Decoders
Author :
Kao, Jonathan C. ; Stavisky, Sergey D. ; Sussillo, David ; Nuyujukian, Paul ; Shenoy, Krishna V.
Author_Institution :
Dept. of Electr. Eng., Stanford Univ., Stanford, CA, USA
Abstract :
Brain-machine interface (BMI) systems convert neural signals from motor regions of the brain into control signals to guide prosthetic devices. The ultimate goal of BMIs is to improve the quality of life for people with paralysis by providing direct neural control of prosthetic arms or computer cursors. While considerable research over the past 15 years has led to compelling BMI demonstrations, there remain several challenges to achieving clinically viable BMI systems. In this review, we focus on the challenge of increasing BMI performance and robustness. We review and highlight key aspects of intracortical BMI decoder design, which is central to the conversion of neural signals into prosthetic control signals, and discuss emerging opportunities to improve intracortical BMI decoders. This is one of the primary research opportunities where information systems engineering can directly impact the future success of BMIs.
Keywords :
brain-computer interfaces; control engineering computing; decoding; handicapped aids; medical signal processing; neurocontrollers; prosthetics; robust control; BMI demonstration; BMI performance; BMI systems; brain-machine interface decoders; computer cursor; direct neural control; information systems engineering; information systems opportunity; intracortical BMI decoder design; motor regions; neural signals; paralysis; prosthetic arms; prosthetic control signal; prosthetic devices; robustness; Algorithm design and analysis; Decoding; Information systems; Kinematics; Man machine systems; Neural networks; Neurons; Neuroscience; Prosthetics; Brain–computer interface (BCI); Brain??computer interface (BCI); brain–machine interface (BMI); brain??machine interface (BMI); control algorithm; decode algorithm; intracortical array; neural network; neural prosthesis;
Journal_Title :
Proceedings of the IEEE
DOI :
10.1109/JPROC.2014.2307357