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Separating Stable Neural Representation Learning from Real-Time Decoding Enables Robust Offline iBCI Performance

M. Mender1, J. Costello1, P. Pinchi1, M.S. Willsey1

1University of Michigan, Ann Arbor, MI, USA

Most high-performance intracortical brain-computer interfaces (iBCIs) require daily calibration because the relationship between neural recordings and movement changes across days. While a stable neural representation, i.e. a neural manifold, of movement can be retrospectively defined by non-linear dynamical models, these models have not been widely implemented in real-time as a ‘decoder’ to translate neural activity into control signals. Instead, high performance real-time iBCIs have been achieved with less complex models (e.g., Kalman filters, temporally convolved neural networks, and LSTMs) that can be trained quickly. Here we demonstrate stable prospective finger-movement predictions with a lightweight LSTM by training on velocity labels derived from a stable cross-session neural representation.

We studied this in a public non-human primate dataset containing 156 sessions spanning 1200 days with recorded intracortical activity in the precentral gyrus during movements of two finger groups. Using Latent Factor Analysis via Dynamical Systems (LFADS) with session-specific input and output layers, we first identified a consistent neural manifold for two-finger movements. When applied to unseen prospective days, standard LFADS had a 36.8% decrease in velocity decoding performance from factors. We therefore adapted LFADS to use a shared read-in layer with session-specific output layers which enabled projecting neural activity from prospective sessions onto the stable manifold. This improved prospective decoding by 43.9% relative to standard LFADS (only 7.7% decrease from training sessions).

We then made two innovations to the LSTM: adding a neural reconstruction loss and training on velocities from LFADS predictions. We hypothesized that using velocities predicted from this stable neural manifold would enable LSTM decoders to learn consistent behavioral information despite neural instabilities. We trained an LSTM with velocities predicted by LFADS factors over 10 sessions and compared this with an LSTM trained on true velocity labels from those sessions. This new method, denoted the ‘stable label method (SLM)’, achieved a 10.2% increase in prospective performance relative to an LSTM trained on recorded velocities (p=0.01). Additional performance gains could also be achieved with unsupervised fine-tuning using SLM on prospective sessions.

By separating the stable representation learning from real-time decoding, SLM provides a blueprint for leveraging more historical data while retaining a model with rapid training and evaluation. This provides a path towards stable high-performance, low-calibration, multi-effector BCIs.