Processing and analysis of multichannel extracellular neuronal signals: state-of-the-art and challenges

Mahmud, M. ORCID: 0000-0002-2037-8348 and Vassanelli, S., 2016. Processing and analysis of multichannel extracellular neuronal signals: state-of-the-art and challenges. Frontiers in Neuroscience, 10: 248. ISSN 1662-453X

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Abstract

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data.

Item Type: Journal article
Publication Title: Frontiers in Neuroscience
Creators: Mahmud, M. and Vassanelli, S.
Publisher: Frontiers Research Foundation
Date: 2 June 2016
Volume: 10
ISSN: 1662-453X
Identifiers:
NumberType
10.3389/fnins.2016.00248DOI
Rights: Copyright © 2016 Mahmud and Vassanelli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Divisions: Schools > School of Science and Technology
Depositing User: Jonathan Gallacher
Date Added: 24 Jul 2018 09:29
Last Modified: 24 Jul 2018 09:29
URI: http://irep.ntu.ac.uk/id/eprint/34147

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