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

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

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

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

NeuroKit2 is a user-friendly package providing easy access to advanced biosignal processing routines. Researchers and clinicians without extensive knowledge of programming or biomedical signal processing can analyze physiological data with only two lines of code.

This package provides tools for calculating smoothed 2D position, speed, head direction.

Elephant (Electrophysiology Analysis Toolkit) is an open-source, community centered library for the analysis of electrophysiological data in the Python programming language. The focus of Elephant is on generic analysis functions for spike train data and time series recordings from electrodes, such as the local field potentials (LFP) or intracellular voltages. In addition to providing a common platform for analysis code from different laboratories, the Elephant project aims to provide a consistent and homogeneous analysis framework that is built on a modular foundation. Elephant is the direct successor to Neurotools and maintains ties to complementary projects such as OpenElectrophy and spykeviewer.

This package provides denoising tools for M/EEG processing in Python.

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.

The OpenMEEG software is a C++ package for solving the forward problems of electroencephalography (EEG) and magnetoencephalography (MEG).

This package provides I/O functions for the CNT format from ANT Neuro.

Meggie is an open-source software designed for intuitive MEG and EEG analysis. With its user-friendly graphical interface, Meggie brings the powerful analysis methods of MNE-Python to researchers without requiring programming skills.

pyEDFlib is a Python library to read/write EDF+/BDF+ files based on EDFlib. EDF means European Data Format

Tensor-based Phase-Amplitude Coupling.

MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-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.

MNE-LSL (Documentation website) provides a real-time brain signal streaming framework. MNE-LSL contains an improved python-binding for the Lab Streaming Layer C++ library, mne_lsl.lsl, replacing pylsl. This low-level binding is used in high-level objects to interact with LSL streams.

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.

Fast, efficient, and physiologically-informed tool to parameterize neural power spectra

SESAMEEG is a Python3 library providing the Bayesian multi-dipole localization method SESAME for the automatic estimation of brain source currents from MEEG data, either in the time domain and in the frequency domain.

This package provides additional functionality for working with MNE-Python, the most popular Python package for processing electrophysiological data (EEG, MEG, ...).

Features:

• Reading additional file formats

• Inspecting files before reading

• Writing raw data

• ICLabel classification

This package provides a Python implementation of a multitaper window method for estimating Wigner spectra for certain locally stationary processes.

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

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