Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82912
Title: Design of a hybrid neural spike detection algorithm for implantable integrated brain circuits
Authors: Zeinolabedin, Seyed Mohammad Ali
Do, Anh Tuan
Yeo, Kiat Seng
Kim, Tony Tae-Hyoung
Keywords: Spike
Sorting
Integrated brain circuits interface
CMOS
Subthreshold
Issue Date: 2015
Source: Zeinolabedin, S. M. A., Do, A. T., Yeo, K. S., & Kim, T. T.-H. (2015). Design of a hybrid neural spike detection algorithm for implantable integrated brain circuits. 2015 IEEE International Symposium on Circuits and Systems (ISCAS), 794-797.
Abstract: Real time spike detection is the first critical step to develop spike-sorting for integrated brain circuits interface applications. Nonlinear Energy Operator (NEO) and absolute thresholding have been widely used as the spike detection algorithms where NEO has a better performance measured by the probability of detection and false alarm. This paper proposes a hybrid spike detection algorithm incorporating both spike detection algorithms to reduce the power and to keep the detection rate the same as that of NEO. In the proposed algorithm, the absolute thresholding is performed first to detect a potential spike. Once a potential spike is detected, NEO is executed to check whether the detected spike by absolute thresholding is valid. Since NEO is conditionally conducted, this reduces the overall power consumption. The simulation shows that the proposed hybrid method improves the power consumption by 54.48% compared to NEO in 65 nm CMOS technology.
URI: https://hdl.handle.net/10356/82912
http://hdl.handle.net/10220/40373
DOI: 10.1109/ISCAS.2015.7168753
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ISCAS.2015.7168753].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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