Sodagar Amir M, Khazaei Yousef, Nekoui Mahdi, Shaeri MohammadAli
Integrated Electronic (INTELECT) Research Laboratory, EECS Department, York University, Toronto, ON, Canada.
Institute of Electrical and Micro Engineering, Center for Neuroprosthetics, EPFL, Geneva, 1202, Switzerland.
Bioelectron Med. 2025 Jul 19;11(1):17. doi: 10.1186/s42234-025-00177-6.
Recent advances in the development of intra-cortical neural interfacing devices show the bright horizon of having access to brain-implantable microsystems with extremely high channel counts in the not-so-distant future. With the fabrication of high-density neural interfacing microelectrode arrays, the handling of the neural signals recorded from the brain is becoming the bottleneck in the realization of next generation wireless brain-implantable microsystems with thousands of parallel channels. Even though a spectrum of engineering efforts has been reported for this purpose at both system and circuit levels, it is now apparent that the most effective solution is to resolve this problem at the signal level. Employment of digital signal processing techniques for data reduction or compression has therefore become an inseparable part of the design of a high-density neural recording brain implant. This paper first addresses technical and technological challenges of transferring massive amount of recorded data off high-density neural recording brain implants. It then provides an overview of the 'on-implant signal processing' techniques that have been employed to successfully stream neuronal activities off the brain. What distinguishes this class of signal processing from signal processing in general is the critical importance of hardware efficiency in the implementation of such techniques in terms of power consumption, circuit size, and real-time operation. The focus of this review is on spike detection and extraction, temporal and spatial neural signal compression, and spike sorting.
皮质内神经接口设备开发的最新进展表明，在不远的将来，有望获得具有极高通道数的可植入大脑的微系统。随着高密度神经接口微电极阵列的制造，处理从大脑记录的神经信号正成为实现具有数千个并行通道的下一代无线可植入大脑微系统的瓶颈。尽管在系统和电路层面都已报道了为此目的所做的一系列工程努力，但现在很明显，最有效的解决方案是在信号层面解决这个问题。因此，采用数字信号处理技术进行数据缩减或压缩已成为高密度神经记录脑植入物设计中不可或缺的一部分。本文首先探讨了从高密度神经记录脑植入物中传输大量记录数据所面临的技术和工艺挑战。然后概述了已被用于成功从大脑中流式传输神经元活动的“植入式信号处理”技术。这类信号处理与一般信号处理的区别在于，在功耗、电路尺寸和实时操作方面，硬件效率在实施此类技术时至关重要。本综述的重点是尖峰检测与提取、时空神经信号压缩以及尖峰分类。
