  While it is important to make implantable brain-machine interfaces (iBMI)
wireless to increase patient comfort and safety, the trend of increased channel
count in recent neural probes poses a challenge due to the concomitant increase
in the data rate. Extracting information from raw data at the source by using
edge computing is a promising solution to this problem, with integrated
intention decoders providing the best compression ratio. In this work, we
compare different neural networks (NN) for motor decoding in terms of accuracy
and implementation cost. We further show that combining traditional signal
processing techniques with machine learning ones deliver surprisingly good
performance even with simple NNs. Adding a block Bidirectional Bessel filter
provided maximum gains of $\approx 0.05$, $0.04$ and $0.03$ in $R^2$ for
ANN\_3d, SNN\_3D and ANN models, while the gains were lower ($\approx 0.02$ or
less) for LSTM and SNN\_streaming models. Increasing training data helped
improve the $R^2$ of all models by $0.03-0.04$ indicating they have more
capacity for future improvement. In general, LSTM and SNN\_streaming models
occupy the high and low ends of the pareto curves (for accuracy vs.
memory/operations) respectively while SNN\_3D and ANN\_3D occupy intermediate
positions. Our work presents state of the art results for this dataset and
paves the way for decoder-integrated-implants of the future.
