Low SNR neural spike detection using scaled energy operators for implantable brain circuits.

Tariq, Taimoor and Satti, Muhammad Hashim and Saeed, Maryam and Kamboh, Awais Mehmood (2017) Low SNR neural spike detection using scaled energy operators for implantable brain circuits. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2017. pp. 1074-1077. ISSN 1557-170X

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Abstract

Real time on-chip spike detection is the first step in decoding neural spike trains in implantable brain machine interface systems. Nonlinear Energy Operator (NEO) is a transform widely used to distinguish neural spikes from background noise. In this paper we define a general form of energy operators, of which NEO is a specific example, which gives better spike-noise separation than NEO and its derivatives. This is because of a non-linear scaling applied to the general discrete energy operator. Using two well-known publically available datasets, the performance of several operators is compared. On data sets that contain multi-unit spikes with low Signal to Noise ratio, the detection accuracy was improved by approximately 15%.

Item Type: Article
Subjects: R Medicine > RC Internal medicine
Divisions: University of Khartoum > Faculty of Medicine, Health and Life Sciences > Department of Medicine
Depositing User: Unnamed user with email almegdadsharaf@gmail.com
Date Deposited: 31 Jul 2018 14:47
Last Modified: 31 Jul 2018 14:47
URI: http://search.srh.edu.sd/id/eprint/5024

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