Abstract
This paper discusses a multi-scale neural spike detection algorithm for a low-power analog circuit implementation. The key idea is to implement wavelet decomposition and improve spike detection by independently controlling thresholds for each scale. Each thresholder scale is then combined to provide a single output indicating a spike occurrence. This spike detection algorithm shows promising results towards a robust, compact, and unsupervised low power analog spike detection circuit. A low power front-end spike detection circuit can be added to a neural amplifier and dramatically reduce the required data bandwidth for BMI applications. © 2005 IEEE.
Recommended Citation
C. L. Rogers et al., "An Analog VLSI Implementation Of A Multi-scale Spike Detection Algorithm For Extracellular Neural Recordings," 2nd International IEEE EMBS Conference on Neural Engineering, vol. 2005, pp. 213 - 216, article no. 1419594, Institute of Electrical and Electronics Engineers, Dec 2005.
The definitive version is available at https://doi.org/10.1109/CNE.2005.1419594
Department(s)
Electrical and Computer Engineering
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Dec 2005
