We present MultiElec, an open source MATLAB based application for data analysis of microelectrode array (MEA) recordings. MultiElec displays an extremely user-friendly graphic user interface (GUI) that allows the simultaneous display and analysis of voltage traces for 60 electrodes and includes functions for activation-time determination, the production of activation-time heat maps with activation time and isoline display. Furthermore, local conduction velocities are semi-automatically calculated along with their corresponding vector plots. MultiElec allows ad hoc signal suppression, enabling

MVPA-Light: A Classification and Regression Toolbox for Multi-Dimensional Data MVPA-Light is a MATLAB toolbox for multivariate pattern analysis (MVPA). It provides native implementations of a range of classifiers and regression models, using modern optimization algorithms. High-level functions allow for the multivariate analysis of multi-dimensional data, including generalization (e.g., time x time) and searchlight analysis. The toolbox performs cross-validation, hyperparameter tuning, and nested preprocessing. It computes various classification and regression metrics and establishes their statistical significance, is modular and easily extendable. Furthermore, it offers interfaces for LIBSVM and LIBLINEAR as well as an integration into the FieldTrip neuroimaging toolbox. After introducing MVPA-Light, example analyses of MEG and fMRI datasets, and benchmarking results on the classifiers and regression models are presented. Published in Frontiers in Neuroscience To use MVPA as part of a neuroimaging analysis pipeline, numerous excellent MATLAB toolboxes have been developed over the years, including the Amsterdam Decoding and Modeling Toolbox (ADAM) (Fahrenfort et al., 2018 ), BCILAB (Kothe and Makeig, 2013 ), Berlin BCI toolbox (Blankertz et al., 2016 ), CoSMoMVPA (Oosterhof et al., 2016 ), Decision Decoding ToolBOX (DDTBOX) (Bode et al., 2019 ), Donders Machine Learning Toolbox (DMLT) ( github.com/distrep/DMLT ), Pattern Recognition for Neuroimaging Toolbox (PRoNTo) (Schrouff et al., 2013 ), and The Decoding Toolbox (TDT) (Hebart et al., 2015 ). Beyond MATLAB, the currently most popular computer languages for machine learning are Python and R, with outstanding toolboxes such as Scikit Learn (Pedregosa et al., 2011 ) for Python and Caret (Kuhn, 2008 ) and MLR (Bischl et al., 2000 ) for R. An interface for FieldTrip (Oostenveld et al., 2011 ) is described in the Methods section. High-level interface : common MVPA tasks such as searchlight analysis and time generalization including cross-validation can be performed with a few lines of MATLAB code. Many of the hyperparameters required by classifiers and regression models are automatically selected by MVPA-Light, taking the burden of hyperparameter selection off the user. It is worth noting that MVPA-Light is a purely statistical toolbox. That is, it assumes that data has been preprocessed with a neuroimaging toolbox and comes in the shape of MATLAB arrays. Then, example analyses are presented using a publicly available Wakeman and Henson ( 2014 , 2015 ) MEEG dataset and the Haxby et al. ( 2001 ) fMRI dataset. Finally, a benchmarking analysis is conducted wherein the computational efficiency of the classifiers and regression models in MVPA-Light is compared to models in other toolboxes in MATLAB, Python, and R. 2. Materials and Methods 2.1. Requirements A standard desktop computer is sufficient to run MVPA-Light. The RAM requirement is dictated by the memory footprint of the dataset. Since some functions operate on a copy of the data, it is recommended that the available RAM exceeds the size of the dataset by at least a factor of two (e.g., 4+ GB RAM for a 2 GB dataset). MVPA-Light is supported by MATLAB 2012a and more recent versions. The Statistics toolbox is required at some points in the toolbox (e.g., for calculating t -values). The cluster permutation test in mv_statistics uses the Image Processing toolbox to extract the clusters. 2.2. Getting Started MVPA-Light is shipped with a set of example scripts (in the /examples subfolder) and an example EEG dataset. For SVM with a RBF kernel, the best performance is obtained in R, followed MATLAB, with both MVPA-Light and Scikit Learn performing worse. Since no hyperparameter tuning was performed, the latter result is most likely due to differences in the default hyperparameters. Figure 5 Mean ERP classification accuracy for the benchmarking analysis using the MEG single-subjects data (averaged across subjects). MVPA-Light is depicted as a solid black line. Tables 2 , 3 show the timing results for different classifiers and regression models. For linear SVM, LIBLINEAR yields the best training speed except for the fMRI data, where MVPA-Light performs best. For RBF Overall, the fastest classifier is MVPA-Light's LDA and the fastest regression model is MVPA-Light's Ridge Regression. Partially, the success of MVPA-Light is due to specialization: MVPA-Light models tend to have fewer hyperparameters than other models, and MVPA-Light features separate optimized implementations for binary LDA and multi-class LDA, whereas the other toolboxes have a single implementation. Furthermore, MVPA-Light's LDA and Ridge Regression dynamically switch between primal and dual form. This can increase computational efficiency especially when dealing with a large dataset. The benchmarking results should not be interpreted as final verdicts on the respective toolboxes. Undoubtedly, training speed can be improved by finding an optimal set of hyperparameters for a model. For instance, increasing regularization tends to lead to smoother loss surfaces and often faster convergence for gradient descent algorithms. The strategy for the present analysis was to change default parameters minimally and, if so, only in order to increase comparability e.g., by setting a regularization parameter to a common value. 10.1016/j.jneumeth.2007.03.024 Pubmed Abstract CrossRef Google Scholar View reference in article 33 Mika S. Ratsch G. Weston J. Schölkopf B. Müller K.-R. ( 1999 ). “Fisher discriminant analysis with kernels,” in Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468) ( IEEE ), 41 – 48 . Pubmed Abstract Google Scholar View reference in article 34 Misaki M. Kim Y. Bandettini P. A. Kriegeskorte N. ( 2010 ). Comparison of multivariate classifiers and response normalizations for pattern-information fMRI . Neuroimage 53 , 103 – 118 .

Population decoding is a powerful way to analyze neural data, however, currently only a small percentage of systems neuroscience researchers use this method. In order to increase the use of population decoding, we have created the Neural Decoding Toolbox (NDT) which is a Matlab package that makes it easy to apply population decoding analyses to neural activity. The design of the toolbox revolves around four abstract object classes which enables users to interchange particular modules in order to try different analyses while keeping the rest of the processing stream intact. The toolbox is capab

Example template for network analyses using matlab inside brainlife.

The purpose of this document is to help individuals use the "Essential Motor Cortex Signal Processing MATLAB Toolbox". The toolbox implements various methods for three major aspects of investigating human motor cortex from Neuroscience view point: (1) ERP estimation and quantification, (2) Cortical Functional Connectivity analysis and (3) EMG quantification. The toolbox -- which is distributed under the terms of the GNU GENERAL PUBLIC LICENSE as a set of MATLAB R routines -- can be downloaded directly at the address: http://oset.ir/category.php?dir=Tools or from the public repository on GitHub, at address below: https://github.com/EsiSeraj/ERP Connectivity EMG Analysis The purpose of this toolbox is threefold: 1. Extract the event-related-potential (ERP) from preprocessed cerebral signals (i.e. EEG, MEG, etc.), identify and then quantify the event-related synchronization/desynchronization (ERS/ERD) events. Both time-course dynamics and time-frequency (TF) analyzes are included. 2. Measure, quantify and demonstrate the cortical functional connectivity (CFC) across scalp electrodes. These set of functions can also be applied to various types of cerebral signals (i.e. electric and magnetic). 3. Quantify electromyogram (EMG) recorded from active muscles during performing motor tasks.