Building interactive web applications with R is incredibly easy with Shiny. Behind the scenes, Shiny builds a reactive graph that can quickly become intertwined and difficult to debug. The reactlog package provides a visual insight into that black box of Shiny reactivity by constructing a directed dependency graph of the application's reactive state at any time point in a reactive recording.

Redox is a C++ interface to the Redis key-value store that makes it easy to write applications that are both elegant and high-performance. Communication should be a means to an end, not something we spend a lot of time worrying about. Redox takes care of the details so you can move on to the interesting part of your project.

GHDL analyses, elaborates and simulates VHDL sources. It may also be used as an experimental synthesizer backend.

This package gathers GNAT binaries from FSF GCC releases of the Alire Project.

UVVM Light is a low threshold version of UVVM and is intended for developers who want to start using UVVM Utilty library and Bus Functional Models.

pyVHDLModel provides an unified abstract language model for VHDL written in Python.

This plugin provides a shared library module for Yosys to implement logical synthesis of VHDL designs.

GHDL Language Server Protocol (LSP) is a server for VHDL based on GHDL.

This package provides a new backend based on pyqtgraph for the 2D-Data-Browser in MNE-Python.

Python library for reading, writing, and validating SNIRF files

This package provides code for feature extraction with M/EEG data.

This package provides an electrophysiological data analysis library for Python.

A Python package to handle the layout, geometry, and wiring of silicon probes for extracellular electrophysiology experiments.

mne-denoise provides powerful signal denoising techniques for the MNE-Python ecosystem, including Denoising Source Separation (DSS) and ZapLine algorithms. These methods excel at extracting signals of interest by exploiting data structure rather than just variance.

This package provides support for reading and writing EEGLAB files in Python.

A simple python package for fitting L2- and smoothing-penalized generalized linear models. Built primarily because the statsmodels GLM fit_regularized method is built to do elastic net (combination of L1 and L2 penalities), but if you just want to do an L2 or a smoothing penalty (like in generalized additive models), using a penalized iteratively reweighted least squares (p-IRLS) is much faster.

This is a library to automatically reject bad trials and repair bad sensors in magneto-/electroencephalography (M/EEG) data.

MNE-Connectivity is an open-source Python package for connectivity and related measures of MEG, EEG, or iEEG data built on top of the MNE-Python API. It includes modules for data input/output, visualization, common connectivity analysis, and post-hoc statistics and processing.

This package implements both parametric and permutation-based ARI, and is meant to be compatible with the MNE-Python ecosystem.

XDF is a general-purpose container format for multi-channel time series data with extensive associated meta information. XDF is tailored towards biosignal data such as EEG, EMG, EOG, ECG, GSR, MEG, but it can also handle data with high sampling rate (like audio) or data with a high number of channels (like fMRI or raw video). Meta information is stored as XML.

This package provides utilities for reading the files produced by BIOPAC's AcqKnowledge software.

BioSPPy is a toolbox for biosignal processing written in Python. The toolbox bundles together various signal processing and pattern recognition methods geared torwards the analysis of biosignals.

YASA is a Python package to analyze polysomnographic sleep recordings.

HED is a framework for systematically describing both laboratory and real-world events as well as other experimental metadata. HED tags are comma-separated path strings that provide a standardized vocabulary for annotating events and experimental conditions.

Key Features:

• Validate HED annotations against schema specifications

• Analyze and summarize HED-tagged datasets

• Full HED support in BIDS (Brain Imaging Data Structure)

• HED support in NWB (Neurodata Without Borders) when used the ndx-hed extension.

• Platform-independent and data-neutral

• Command-line tools and Python API