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.

翻译：尽管植入式脑机接口(iBMI)的无线化对提升患者舒适度与安全性至关重要，但近期神经探针通道数的增加趋势带来了数据速率同步攀升的挑战。利用边缘计算在源头从原始数据中提取信息是解决这一问题的可行方案，其中集成意图解码器能实现最佳压缩比。本研究从准确率与实现成本两方面比较了不同神经网络(NN)在运动解码中的表现。我们进一步证明，将传统信号处理技术与机器学习方法相结合，即便使用简单神经网络也能获得令人惊讶的优异性能。添加块结构双向贝塞尔滤波器使ANN\_3d、SNN\_3D与ANN模型的$R^2$值最大提升约$0.05$、$0.04$和$0.03$，而LSTM和SNN\_streaming模型的提升幅度较小（约$0.02$或更低）。增加训练数据可使所有模型的$R^2$值提升$0.03-0.04$，表明其具备更大的改进潜力。总体而言，LSTM与SNN\_streaming模型分别占据帕累托曲线（准确率与内存/运算量的权衡曲线）的高端与低端，而SNN\_3D与ANN\_3D模型处于中间位置。本研究在该数据集上取得了当前最优结果，为未来解码器集成植入式设备奠定了基础。