  Intracortical brain computer interfaces (iBCIs) using linear Kalman decoders
have enabled individuals with paralysis to control a computer cursor for
continuous point-and-click typing on a virtual keyboard, browsing the internet,
and using familiar tablet apps. However, further advances are needed to deliver
iBCI-enabled cursor control approaching able-bodied performance. Motivated by
recent evidence that nonlinear recurrent neural networks (RNNs) can provide
higher performance iBCI cursor control in nonhuman primates (NHPs), we
evaluated decoding of intended cursor velocity from human motor cortical
signals using a long-short term memory (LSTM) RNN trained across multiple days
of multi-electrode recordings. Running simulations with previously recorded
intracortical signals from three BrainGate iBCI trial participants, we
demonstrate an RNN that can substantially increase bits-per-second metric in a
high-speed cursor-based target selection task as well as a challenging
small-target high-accuracy task when compared to a Kalman decoder. These
results indicate that RNN decoding applied to human intracortical signals could
achieve substantial performance advances in continuous 2-D cursor control and
motivate a real-time RNN implementation for online evaluation by individuals
with tetraplegia.
