A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)

MNELAB – a GUI for MNE-Python

Automatic labeling of ICA components in Python.

ICA-AROMA Software Package: a data-driven method to identify and remove head motion-related artefacts from functional MRI data.

Group ICA/IVA software (MATLAB)

InkCanvasForClass Community Open Source Project

Automatic EEG IC classification plugin for EEGLAB

VAEs and nonlinear ICA: a unifying framework

Fusion ICA Toolbox (MATLAB)

Process and analyse EEG brain signals using EEGlab

Using computational tools to explore the networks underlying cognitive neuroscience

🍻 A pub quiz web application built using the MERN stack with websockets for near real-time quiz action

Python implementation of a stabilized ICA algorithm

This is the code for the paper Embrace the Gap: VAEs perform Independent Mechanism Analysis, showing that optimizing the ELBO is equivalent to optimizing the IMA-regularized log-likelihood under certain assumptions (e.g., small decoder variance).

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

This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (particularly, with Nonlinear ICA) can be used to extract the causal graph from an underlying structural equation model (SEM).

Robust electrophysiology tools with GPU acceleration

Create optimized ICA training (OPTICAT) data for EEG data recorded during free viewing (Dimigen, 2020, NeuroImage)

Discovering Universal Geometry in Embeddings with ICA (Published in EMNLP 2023)

Matrix decomposition algorithms including PCA (principal component analysis) and ICA (independent component analysis)

Repository that gathers code for signal processing

No description provided.

EEGPrep is an automated preprocessing tool for human EEG data built on a benchmarked EEGLAB pipeline

Reusable neuroimaging pipelines using nipype

A blind source separation package using non-negative matrix factorization and non-negative ICA

Independent component analysis for dimensionality reduction of hyperspectral images

A python based Machine Learning project that separates two sounds intermixed.

This repository contains the collection of Cognitive Science computation modeling projects made for the DTU Human-Centered AI course 02458: Cognitive Modeling

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

My notes for Prof. Klaus Obermayer's "Machine Intelligence 2 - Unsupervised Learning" course at the TU Berlin