In Table 1 we provide a summary of the toolboxes we consider, we list all toolboxes with a brief description in alphabetical order in section 7 with paper reference and downloading link. We include into our comparison major open-source 3 toolboxes (see Table 1) for spike and LFP data processing and analysis which have a valid link for downloading, documentation, scientific paper describing the toolbox's features or corresponding method, and which were updated in the last 5 years. indicate bibliometric information 2: while popularity among users alone does not guarantee quality, it can be an important indicator that toolbox's functions are easy-to-use and have been tested.

provide information about documentation and support quality for the toolboxes overview specialized tools for dimensionality reduction and generalized linear modeling as they are widely used in neuroscientific research ( Truccolo et al., 2005 Cunningham and Byron, 2014) For readers' convenience we make the corresponding MATLAB scripts publicly available 1 compare and illustrate functionality of the toolboxes on open-access datasets ( Lowet et al., 2015 Lawlor et al., 2018 Perich et al., 2018) and simulated data. compare in detail common and discuss unique functionality of toolboxes include in the comparison important toolboxes and tools not covered by earlier reviews (e.g., Brainstorm, Elephant and FieldTrip) Compared to the existing reviews ( Ince et al., 2009, 2010 Ince, 2012 Mahmud and Vassanelli, 2016 Timme and Lapish, 2018), we We aim to provide a practical guidance for choosing a proper toolbox on the basis of toolbox functionality, statistical and visualization tools, programming language, availability of graphical user interface, support and documentation quality. Often neuroscientists are even not aware that some functionality is already implemented and start writing their own scripts from scratch which takes time and is error-prone. However, ascertaining whether functionality of the toolbox fits users' requirements is in many cases time-consuming. There are many already implemented open-source tools and toolboxes for spike and LFP data analysis. To give a better insight, we compare and illustrate functionality of the toolboxes on open-access dataset or simulated data and make corresponding MATLAB scripts publicly available.Īnalysis of spike and local field potential (LFP) data is an essential part of neuroscientific research ( Brown et al., 2004 Stevenson and Kording, 2011 Mahmud and Vassanelli, 2016).

We focus on comparing toolboxes functionality, statistical and visualization tools, documentation and support quality. We overview major open-source toolboxes for spike and LFP data analysis as well as toolboxes with tools for connectivity analysis, dimensionality reduction and generalized linear modeling. Here we aim to provide a practical guidance for neuroscientists in the choice of an open-source toolbox best satisfying their needs. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality.

4Bernstein Center for Computational Neuroscience, Göttingen, GermanyĪnalysis of spike and local field potential (LFP) data is an essential part of neuroscientific research.3Georg-Elias-Mueller-Institute of Psychology, University of Goettingen, Göttingen, Germany.

1Cognitive Neurosciences Laboratory, German Primate Center, Göttingen, Germany.

Matlab toolboxes in octave