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

Abstract: 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.