MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

The MATLAB toolbox for MEG, EEG and iEEG analysis

Deep learning software to decode EEG, ECG or MEG signals

EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD

A list of openly available datasets in (mostly human) electrophysiology.

Parameterizing neural power spectra into periodic & aperiodic components.

A Python Toolbox for Multimode Neural Data Representation Analysis - A Representational Analysis Toolbox for Neuroscience, including Neural Pattern Similarity (NPS), Representational Similarity Analysis (RSA), Spatiotemporal Pattern Similarity (STPS) & Inter-Subject Correlation (ISC)

A list of openly available software tools for (mostly human) electrophysiology.

Package to analyze EEG, ECoG and other electrophysiology formats. It allows for visualization of the results and for a GUI that can be used to score sleep stages.

[PNAS'21] The neural architecture of language: Integrative modeling converges on predictive processing

Systems Neuroscience Computing in Python: user-friendly analysis of large-scale electrophysiology data

Real-time analysis of intracranial neurophysiology recordings.

3D Visualization of Brain MRI

ECoG/EEG neural network decoder that can generalize to unseen participants and recording modalities. Replicates our published results: https://doi.org/10.1088/1741-2552/abda0b.

Methodological considerations for analyzing and interpreting neural oscillations.

FingerFlex: a new state of the art model for prediction finger movements from brain activity (ECoG).

Feature extraction and prediction of human neural signals, using chaos theory, dynamical systems theory, various physics/maths theories, RNN, CNN, transformers

Matlab toolbox for univariate and multivariate analysis of EEG data in the time and frequency domains

This repository provides analysis code to analyze intracranial electrophysiological data with data-driven spatial filters.

Evaluating methods for estimating aperiodic activity in electrophysiological data.