Here are some software toolboxes I have developed for EEG data analysis:

1. EEG Bad Channel Detection Using Local Outlier Factor (LOF)

This toolbox utilizes the Local Outlier Factor (LOF) algorithm for detecting bad channels in EEG data. You can find the source code and documentation at the following links:

  • EEGLAB Toolbox: This repository provides a MATLAB implementation of the LOF-based bad channel detection method, designed to work with EEGLAB.
  • MNE-Python Implementation: The LOF algorithm is also implemented in MNE-Python, a popular Python library for EEG/MEG analysis.

2. NEAR (Newborn EEG Artifact Removal) Pipeline

The NEAR pipeline is designed for artifact removal in newborn EEG data and is compatible with EEGLAB software. You can access the source code and documentation here:

  • NEAR Pipeline for EEGLAB: This repository contains the code for the NEAR pipeline, offering tools for effective artifact removal in newborn EEG data.