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Article

Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings

1
Department of Epileptology, University of Bonn Medical Center, Venusberg-Campus 1, 53127 Bonn, Germany
2
Computational Neuroengineering, Department of Electrical and Computerengineering, TU Munich, 80333 Munich, Germany
3
Institute for Artificial Intelligence and Informatics in Medicine, TU Munich, 80333 Munich, Germany
4
Machine Learning in Science, University of Tübingen, 72076 Tübingen, Germany
5
Department of Neurosurgery, University of Bonn Medical Center, Venusberg-Campus 1, 53127 Bonn, Germany
*
Author to whom correspondence should be addressed.
Both authors contributed equally to this work.
Academic Editors: Yvonne Höller and Ulrich G. Hofmann
Brain Sci. 2021, 11(6), 761; https://doi.org/10.3390/brainsci11060761
Received: 26 February 2021 / Revised: 29 May 2021 / Accepted: 1 June 2021 / Published: 8 June 2021
(This article belongs to the Special Issue Quantitative EEG and Cognitive Neuroscience)
Single-unit recordings in the brain of behaving human subjects provide a unique opportunity to advance our understanding of neural mechanisms of cognition. These recordings are exclusively performed in medical centers during diagnostic or therapeutic procedures. The presence of medical instruments along with other aspects of the hospital environment limit the control of electrical noise compared to animal laboratory environments. Here, we highlight the problem of an increased occurrence of simultaneous spike events on different recording channels in human single-unit recordings. Most of these simultaneous events were detected in clusters previously labeled as artifacts and showed similar waveforms. These events may result from common external noise sources or from different micro-electrodes recording activity from the same neuron. To address the problem of duplicate recorded events, we introduce an open-source algorithm to identify these artificial spike events based on their synchronicity and waveform similarity. Applying our method to a comprehensive dataset of human single-unit recordings, we demonstrate that our algorithm can substantially increase the data quality of these recordings. Given our findings, we argue that future studies of single-unit activity recorded under noisy conditions should employ algorithms of this kind to improve data quality. View Full-Text
Keywords: human single-unit recordings; artifact removal; spike sorting human single-unit recordings; artifact removal; spike sorting
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MDPI and ACS Style

Dehnen, G.; Kehl, M.S.; Darcher, A.; Müller, T.T.; Macke, J.H.; Borger, V.; Surges, R.; Mormann, F. Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings. Brain Sci. 2021, 11, 761. https://doi.org/10.3390/brainsci11060761

AMA Style

Dehnen G, Kehl MS, Darcher A, Müller TT, Macke JH, Borger V, Surges R, Mormann F. Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings. Brain Sciences. 2021; 11(6):761. https://doi.org/10.3390/brainsci11060761

Chicago/Turabian Style

Dehnen, Gert, Marcel S. Kehl, Alana Darcher, Tamara T. Müller, Jakob H. Macke, Valeri Borger, Rainer Surges, and Florian Mormann. 2021. "Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings" Brain Sciences 11, no. 6: 761. https://doi.org/10.3390/brainsci11060761

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