SigMate: a MATLAB-based automated tool for extracellular neuronal signal processing and analysis

Mahmud, M. ORCID: 0000-0002-2037-8348, Bertoldo, A., Girardi, S., Maschietto, M. and Vassanelli, S., 2012. SigMate: a MATLAB-based automated tool for extracellular neuronal signal processing and analysis. Journal of Neuroscience Methods, 207 (1), pp. 97-112. ISSN 0165-0270

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Abstract

Rapid advances in neuronal probe technology for multisite recording of brain activity have posed a significant challenge to neuroscientists for processing and analyzing the recorded signals. To be able to infer meaningful conclusions quickly and accurately from large datasets, automated and sophisticated signal processing and analysis tools are required. This paper presents a Matlab-based novel tool, “SigMate”, incorporating standard methods to analyze spikes and EEG signals, and in-house solutions for local field potentials (LFPs) analysis. Available modules at present are – 1. In-house developed algorithms for: data display (2D and 3D), file operations (file splitting, file concatenation, and file column rearranging), baseline correction, slow stimulus artifact removal, noise characterization and signal quality assessment, current source density (CSD) analysis, latency estimation from LFPs and CSDs, determination of cortical layer activation order using LFPs and CSDs, and single LFP clustering; 2. Existing modules: spike detection, sorting and spike train analysis, and EEG signal analysis. SigMate has the flexibility of analyzing multichannel signals as well as signals from multiple recording sources. The in-house developed tools for LFP analysis have been extensively tested with signals recorded using standard extracellular recording electrode, and planar and implantable multi transistor array (MTA) based neural probes. SigMate will be disseminated shortly to the neuroscience community under the open-source GNU-General Public License.

Item Type: Journal article
Publication Title: Journal of Neuroscience Methods
Creators: Mahmud, M., Bertoldo, A., Girardi, S., Maschietto, M. and Vassanelli, S.
Publisher: Elsevier
Date: 30 May 2012
Volume: 207
Number: 1
ISSN: 0165-0270
Identifiers:
NumberType
10.1016/j.jneumeth.2012.03.009DOI
S0165027012001045Publisher Item Identifier
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 24 Jul 2018 08:47
Last Modified: 24 Jul 2018 08:47
URI: http://irep.ntu.ac.uk/id/eprint/34144

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