A Simulation Study on the Generative Neural Ensemble Decoding Algorithms

Brain-computer interfaces rely on accurate decoding of cortical activity to understand intended action. Algorithms for neural decoding can be broadly categorized into two groups: direct versus generative methods. Two generative models, the population vector algorithm (PVA) and the Kalman filter (KF), have been widely used for many intracortical BCI studies, where KF generally showed superior decoding to PVA. However, little has been known for which conditions each algorithm works properly and how KF translates the ensemble information. To address these questions, we performed a simulation study and demonstrated that KF and PVA worked congruently for uniformly distributed preferred directions (PDs) whereas KF outperformed PVA for non-uniform PDs. In addition, we showed that KF decoded better than PVA for low signal-to-noise ratio (SNR) or a small ensemble size. The results suggest that KF may decode direction better than PVA with non-uniform PDs or with low SNR and small ensemble size.