Computationally efficient fully-automatic online neural spike detection and sorting in presence of multi-unit activity for implantable circuits-Reference-Cited by-同舟云学术

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Computationally efficient fully-automatic online neural spike detection and sorting in presence of multi-unit activity for implantable circuits

Published:2019-10 Issue: Volume:179 Page:104986
ISSN:0169-2607
Container-title:Computer Methods and Programs in Biomedicine
language:en
Short-container-title:Computer Methods and Programs in Biomedicine

Author:

Tariq Taimoor,Satti M. Hashim,Kamboh Hamid M.,Saeed Maryam,Kamboh Awais M.

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications,Software

Reference36 articles.

1. Design of a hybrid neural spike detection algorithm for implantable integrated brain circuits;Zeinolabedin,2015

2. Low SNR neural spike detection using scaled energy operators for implantable brain circuits;Tariq,2017

3. Past, present and future of spike sorting techniques;Rey;Brain Res. Bull.,2015

4. Towards a next generation neural interface: optimizing power, bandwidth and data quality;Eftekhar,2010

5. Online detection and sorting of extracellularly recorded action potentials in human medial temporal lobe recordings, in vivo;Rutishauser;J. Neurosci. Methods,2006

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1. Classification of overlapping spikes using convolutional neural networks and long short term memory;Computers in Biology and Medicine;2022-09

2. From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings;Frontiers in Neuroinformatics;2022-06-13

3. An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions;Scientific Reports;2021-07-06

4. Nearly symmetric orthogonal wavelets for time-frequency-shape joint analysis: Introducing the discrete shapelet transform’s third generation (DST-III) for nonlinear signal analysis;Communications in Nonlinear Science and Numerical Simulation;2021-06

5. An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks;Brain Sciences;2020-11-11

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