The Reliable Central-Nervous-System (CNS)
Interfaces (RCI) effort seeks to demonstrate CNS interfaces that dramatically
extend their performance and lifetime. RCI includes strategies for reliably
recording motor-control information from a variety of sources, such as
single-unit action potentials, local field potentials, electrocorticography
(ECoG) and electroencephalography (EEG). This effort focuses on developing
amputee-relevant behavioral-testing methods to accurately evaluate the
reliability of CNS-interface systems prior to testing in the intended patient
Technical Area #1: Demonstrate clinically viable
high-performance CNS interfaces that achieve the ability to reliably record
motor-control information. Performers developed novel electrode coatings to
decrease inflammation and attract neurons to the device, built novel electrodes
that allowed tissues to grow through the device, and built probe arrays that
would dissolve over time leaving only a fine gauge serpentine wire.
Technical Area #2: Develop clinically viable electronic systems for reliable
CNS-interface systems. This challenge remains a significant technology barrier.
Technical Area #3: Demonstrate clinically viable algorithms and
system-level approaches for reliably decoding motor-control signals from
detected CNS signals. Investments resulted in the demonstration of advanced
decoding algorithms capable of self-calibration and adaptive tuning.
Technical Area #4: Develop novel behavioral testing methods to demonstrate
the amputee-relevant functionality of neural interfaces. Investments were made
in developing biological testbeds for demonstrating system performance in vivo.
Technical Area #5: Demonstrate clinically viable systems that provide
tactile sensory and/or proprioceptive limb feedback via stimulation of the CNS.
Lack of direct neural feedback is significantly limiting the ultimate
performance of brain controlled prosthetic limbs.
RCI is one of three complementary efforts within DARPA’s Reliable Neural-Interface Technology (RE-NET) program aimed at understanding why the performance of neural interfaces degrades over time and developing new high-performance neural interfaces that last the life of the patient.
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