@inproceedings{51f9754386d148778fd71312d90ffc2e,
title = "A Spike-Sorting-Assisted Compressed Sensing Processor for High-Density Neural Interfaces",
abstract = "Brain science research requires high-density neural interfaces, where data transmission is challenging in limited power budget. Compressed sensing is a promising approach to reduce data rate of the spike signal, yet the quality of the reconstructed signal greatly depends on the selection of sensing matrix. This paper proposes a spike-sorting-assisted compressed sensing method that exploits using different sensing matrices according to the cluster classes of spikes. The simulated classification accuracy of the proposed method shows a maximum improvement of 20\%, i.e., from 70\% to 90\%. The average classification accuracy achieves 96.26\% and 93.34\% at a compression ratio of 8 and 16, respectively. Implemented in a 65 nm CMOS process, the proposed processor occupies a core area of 0.073 mm2. The simulated power consumption is 0.931 μW while powered by 0.9 V supply and operating at 24 kHz.",
keywords = "compressed sensing, digital integrated circuits, neural recording, spike sorting",
author = "Qingzhen Wang and Wenxian Gu and Hengchang Bi and Liangjian Lyu and Deli Qiao and Xing Wu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 15th IEEE International Conference on ASIC, ASICON 2023 ; Conference date: 24-10-2023 Through 27-10-2023",
year = "2023",
doi = "10.1109/ASICON58565.2023.10396355",
language = "英语",
series = "Proceedings of International Conference on ASIC",
publisher = "IEEE Computer Society",
editor = "Fan Ye and Ting-Ao Tang",
booktitle = "Proceedings of 2023 IEEE 15th International Conference on ASIC, ASICON 2023",
address = "美国",
}