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Intracortical brain computer interfaces (iBCIs) using linear Kalman decodershave enabled individuals with paralysis to control a computer cursor forcontinuous point-and-click typing on a virtual keyboard, browsing the internet,and using familiar tablet apps. However, further advances are needed to deliveriBCI-enabled cursor control approaching able-bodied performance. Motivated byrecent evidence that nonlinear recurrent neural networks (RNNs) can providehigher performance iBCI cursor control in nonhuman primates (NHPs), weevaluated decoding of intended cursor velocity from human motor corticalsignals using a long-short term memory (LSTM) RNN trained across multiple daysof multi-electrode recordings. Running simulations with previously recordedintracortical signals from three BrainGate iBCI trial participants, wedemonstrate an RNN that can substantially increase bits-per-second metric in ahigh-speed cursor-based target selection task as well as a challengingsmall-target high-accuracy task when compared to a Kalman decoder. Theseresults indicate that RNN decoding applied to human intracortical signals couldachieve substantial performance advances in continuous 2-D cursor control andmotivate a real-time RNN implementation for online evaluation by individualswith tetraplegia.

BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulation