🧠⚡A CLI to access and process the THINGS-EEG2 dataset by Gifford et al.

Interface cerveau-machine (EEG) avec Machine Learning. Préprocessing MNE, réduction de dimension (PCA/CSP), pipeline scikit-learn, classification en “temps réel”, validation croisée et évaluation multi-sujets. Projet 42 orienté data science appliquée aux signaux cérébraux.

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

MNELAB – a GUI for MNE

A Python package to write data processing pipelines with. Initially designed for eeg/meg analysis using MNE-Python with MNE-BIDS.

PyPREP: A Python implementation of the Preprocessing Pipeline (PREP) for EEG data

I/O support for EEGLAB files in Python.

HCSO submission to Eurostat MNE Discovery Challenge (https://statistics-awards.eu/competitions/23)

This repository hosts a Jupyter notebook for loading, preprocessing, and analyzing the BigP3BCI dataset, focusing on P300 oddball event‑related potentials (ERPs) in an EEG brain–computer interface (BCI) paradigm.

Code for the tms-nirs-connectivity study.

User-friendly MNE-python based graphical user interface to do MEG and EEG analysis with multiple subjects

🧠 EEG preprocessing and classification pipeline for ASD research using MUSE EEG data

Sampling frames for Flemish Natura 2000 (n2k) habitat (hab) monitoring programmes

Average brain volume + labeling

This project analyzes EEG data from 10 subjects—including 5 normal, 3 insomniacs, and 2 narcoleptic—sourced from the CAP Sleep Database. We focus on extracting, visualizing, and comparing sleep stage patterns using EEG signal processing and various analysis techniques.

Preprocessing the fNIRS data from the paper "The use of broad vs restricted regions of interest in functional near-infrared spectroscopy for measuring cortical activation to auditory-only and visual-only speech"

Python routines for parsing of combined EEG and eye-tracking data

classification and dim reduction methods with traditional machine learning techniques

EEG Classification using MiniRocket + Ridge Classifier on BCI Competition IV Dataset 2a. Reproducible pipeline for Time-Series feature extraction and classification.