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.

此研究通过比较不同的神经网络进行运动解码，并结合传统信号处理技术与机器学习方法，在原始数据源处提取信息以解决无线可植入脑机接口中数据速率增加的问题，获得了令人惊喜的性能表现。

ANN vs SNN：植入脑机接口中神经解码的案例研究

ANN vs SNN: A case study for Neural Decoding in Implantable  Brain-Machine Interfaces

Terahertz (THz)-band communications are a key enabler for future-generation
wireless communications systems that promise to integrate a wide range of
data-demanding applications. Recent advances in photonic, electronic, and
plasmonic technologies are closing the gap in THz transceiver design.
Consequently, prospect THz signal generation, modulation, and radiation methods
are converging, and corresponding channel model, noise, and hardware-impairment
notions are emerging. Such progress establishes a foundation for well-grounded
research into THz-specific signal processing techniques for wireless
communications. This tutorial overviews these techniques, emphasizing
ultra-massive multiple-input multiple-output (UM-MIMO) systems and
reconfigurable intelligent surfaces, vital for overcoming the distance problem
at very high frequencies. We focus on the classical problems of waveform design
and modulation, beamforming and precoding, index modulation, channel
estimation, channel coding, and data detection. We also motivate signal
processing techniques for THz sensing and localization.

介绍了基于 Terahertz 波段的通信研究，特别关注在超大规模多输入多输出系统和可重构智能表面上解决高频距离问题所采用的技术。包括传统的波形设计、调制、波束成形、信道估计、信道编码和数据检测，同时提出适用于 THz 感知和定位的信号处理技术。