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MNE Scan: Software for real-time processing of electrophysiological data

July 9, 2020

In a 2018 Journal of Neuroscience Methods article, Lorenz Esch and colleagues present MNE Scan, a software that provides real-time acquisition and processing of electrophysiological data.

MNE Scan is a state-of-the-art real-time processing software for clinical MEG and EEG data. By allowing for real-time analysis of neuronal activity, MNE Scan enables the optimization of input stimuli and permits the use of neurofeedback. MNE Scan is based on the open-source MNE-CPP library. Written in C++, MNE-CPP is a software framework that processes standard electrophysiological data formats and is compatible with Windows, Mac, and Linux. Compared to other open-source real-time electrophysiological processing software, MNE Scan is designed to meet medical regulatory requirements such as the IEC 62304. This makes MNE Scan ideal for clinical studies and is already in active use with an FDA approved pediatric MEG system. MNE Scan has also been validated in several different use cases, making it a robust solution for the processing of MEG and EEG data in a variety of scenarios.

Read more in the paper here!

Or check it out right from their website!


April 30, 2020

Jeffrey P. Gill and colleagues have developed and shared a new toolbox for synchronizing video and neural signals, cleverly named neurotic!

Collecting neural data and behavioral data are fundamental to behavioral neuroscience, and the ability to synchronize these data streams are just as important as collecting the information in the first place. To make this process a little simpler, Gill et al. developed an open-source option called neurotic, a NEUROscience Tool for Interactive Characterization. This tool is programmed in Python and includes a simple GUI, which makes it accessible for users with little coding experience. Users can read in a variety of file formats for neural data and video, which they can then process, filter, analyze, annotate and plot. To show the effectiveness across species and signal types, the authors tested the software with aplysia feeding behavior and human beam walking. Given its open-source nature and strong integration of other popular open-source packages, this software will continue to develop and improve as the community uses it.

Read more about neurotic here!

Check out the documentation here.

Gill, J. P., Garcia, S., Ting, L. H., Wu, M., & Chiel, H. J. (2020). Neurotic: Neuroscience Tool for Interactive Characterization. Eneuro. doi:10.1523/eneuro.0085-20.2020

Toolboxes for Spike and LFP Analysis

April 9, 2020

There are a number of open source toolboxes available for neural data analysis, especially for spike and local field potential data. With more options comes a more difficult decision when it comes to selecting the toolbox that’s right for your data. Fortunately, Valentina Unakafova and Alexander Gail have compared several toolboxes for spike and LFP analysis, connectivity analysis, dimensionality reduction, and generalized linear modeling. They discuss the major features of software available for Python and MATLAB (Octave) including Brainstorm, Chronux, Elephant, FieldTrip, gramm, Spike Viewer, and SPIKY. They include succinct tables for assessing system and program requirements, quality of documentation and support, and data types accepted by each toolbox. Using an open-access dataset, they assess the functionality of the programs and finish their comparison with highlighting advantages of each toolbox to consider when trying to find the one that works best for your data. The files they used to compare toolboxes are all available from GitHub to supplement their paper.

Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research.

Read their full comparison here.

Check out their GitHub for the project here.


June 20, 2019

Ahmet Arac from Peyman Golshani’s lab at UCLA recently developed DeepBehavior, a deep-learning toolbox with post processing methods for video analysis of behavior:

Recently, there has been a major push for more fine-grained and detailed behavioral analysis in the field of neuroscience. While there are methods for taking high-speed quality video to track behavior, the data still needs to be processed and analyzed. DeepBehavior is a deep learning toolbox that automates this process, as its main purpose is to analyze and track behavior in rodents and humans.

The authors provide three different convolutional neural network models (TensorBox, YOLOv3, and OpenPose) which were chosen for their ease of use, and the user can decide which model to implement based on what experiment or what kind of data they aim to collect and analyze. The article provides methods and tips on how to train neural networks with this type of data, and gives methods for post-processing of image data.

In the manuscript, the authors give examples of utilizing DeepBehavior in five behavioral tasks in both animals and humans. For rodents, they use a food pellet reaching task, a three-chamber test, and social interaction of two mice. In humans, they use a reaching task and a supination / pronation task. They provide 3D kinematic analysis in all tasks, and show that the transfer learning approach accelerates network training when images from the behavior videos are used. A major benefit of this tool is that it can be modified and generalized across behaviors, tasks, and species. Additionally, DeepBehavior uses several different neural network architectures, and uniquely provides post-processing methods for 3D kinematic analysis, which separates it from previously published toolboxes for video behavioral analysis. Finally, the authors emphasize the potential for using this toolbox in a clinical setting with analyzing human motor function.


For more details, take a look at their project’s Github.

All three models used in the paper also have their own Github: TensorBox, YOLOv3, and openpose.

Arac, A., Zhao, P., Dobkin, B. H., Carmichael, S. T., & Golshani, P. (2019). DeepBehavior: A deep learning toolbox for automated analysis of animal and human behavior imaging data. Frontiers in systems neuroscience, 13.


Low Cost Open Source Eye Tracking

May 30, 2019

On Hackaday, John Evans and colleagues have shared a design and build for an open-source eye-tracking system for human research.

We’ve wanted to expand our coverage of behavioral tools to include those used in human research. To get this rolling, we’d like to highlight a project for eye tracking that might be helpful to many labs, especially if you don’t have a grant to collect pilot data. Check out Low Cost Open Source Eye Tracking. It uses open-source code, available from GitHub, and a pair of cheap USB cameras.

Check out the details on Hackaday.io and GitHub!

Evans, J. (2018). Low Cost Open Source Eye Tracking. Retrieved from https://hackaday.io/project/153293-low-cost-open-source-eye-tracking

Mousetrap: An integrated, open-source computer mouse-tracking package

October 31, 2016

Mousetrap, an open-source software plugin to record and analyze mouse movements in computerized lab experiments, was developed by Pascal Kieslich and Felix Henninger, both located in Germany.

Mousetrap is a plugin that is used with OpenSesame software for mouse-tracking, or the analysis of mouse movements during computerized lab experiments which can serve as an indicator of commitment or conflict in decision making. The integration of Mousetrap with a general-purpose graphical experiment builder also allows users to access other core features and software extensions of OpenSesame, which offers more flexibility to users when designing experiments. Mousetrap is available for use across all platforms (Linux, Windows and Mac) and the data collected with the software can also be imported directly into R for analysis with an available Mousetrap package.

The GitHub for this project may be found here.