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!
Esch, L., Sun, L., Klüber, V., Lew, S., Baumgarten, D., Grant, P. E., … Dinh, C. (2018). MNE Scan: Software for real-time processing of electrophysiological data. Journal of Neuroscience Methods, 303, 55–67. https://doi.org/10.1016/j.jneumeth.2018.03.020
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
JANUARY 23, 2020
Simon Nilsson from Sam Golden’s lab at the University of Washington recently shared their project SimBA (Simple Behavioral Analysis), an open source pipeline for the analysis of complex social behaviors:
“The manual scoring of rodent social behaviors is time-consuming and subjective, impractical for large datasets, and can be incredibly repetitive and boring. If you spend significant time manually annotating videos of social or solitary behaviors, SimBA is an open-source GUI that can automate the scoring for you. SimBA does not require any specialized equipment or computational expertise.
SimBA uses data from popular open-source tracking tools in combination with a small amount of behavioral annotations to create supervised machine learning classifiers that can then rapidly and accurately score behaviors across different background settings and lighting conditions. Although SimBA is developed and validated for complex social behaviors such as aggression and mating, it has the flexibility to generate classifiers in different environments and for different behavioral modalities. SimBA takes users through a step-by-step process and we provide detailed installation instructions and tutorials for different use case scenarios online. SimBA has a range of in-built tools for video pre-processing, accessing third-party tracking models, and evaluating the performance of machine learning classifiers. There are also several methods for in-depth visualizations of behavioral patterns. Because of constraints in animal tracking tools, the initial release of SimBA is limited to processing social interactions of differently coat colored animals, recorded from a top down view, and future releases will advance past these limitations. SimBA is very much in active development and a manuscript is in preparation. Meanwhile, we are very keen to hear from users about potential new features that would advance SimBA and help in making automated behavioral scoring accessible to more researchers in behavioral neuroscience.”
For more information on SimBA, you can check out the project’s Github page here.
For those looking to contribute or try out SimBA and are looking for feedback, you can interact on the project’s Gitter page.
Plus, take a look at their recent twitter thread detailing the project.
If you would like to be added to the project’s listserv for updates, fill out this form here.