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

End-to-End EEG Pipeline for cleaning, filtering, ICA, mass-univariate, and decoding analysis using MNE python

MNE-preprocessing is a python repository to reduce artifacts based on basic and unanimous approaches step by step from electroencephalographic (EEG) raw data.

Robust electrophysiology tools with GPU acceleration

EEGLAB-compatible analysis software for manual / visual sleep stage scoring, signal processing and event marking of polysomnographic (PSG) data for MATLAB.

General pipeline used for analyzing EEG data where Raw EEG data gets transformed into ERPS and Stats are done in R (Mixed effects models)

EEG/MEG data preprocessing and analyses framework

EEG quality analysis pipeline

BIDSAlign a library for preprocessing and align multiple datasets to a common template.

Automated Python-based Resting-State EEG Preprocessing

Investigating the effect of developmental language disorder (DLD) on the understanding of the quantity system in the Estonian language in Estonian children.

EEG signal preprocessing is essential for improving the quality of brain signals by removing noise and artifacts. It helps isolate meaningful features, making the data more suitable for analysis and classification. This step significantly boosts the performance and adaptability of BCI systems in real-world scenarios.

Notebooks for cleaning, filtering, and performing ICA, Mass-univariate and Decoding analysis

Pipeline for curating intracranial EEG data (2022-2025)

Graph Analysis of Brain Connections in Epilepsy Patients