Repairing artifacts with ICA — MNE 0.22.0 documentation

This tutorial covers the basics of independent components analysis (ICA) and shows how ICA can be used for artifact repair; an extended example illustrates repair of ocular and heartbeat artifacts.

Note Go to the end to download the full example code. This tutorial covers the basics of independent components analysis (ICA) and shows how ICA can be used for artifact repair; an extended example illustrates repair of ocular and heartbeat artifacts. For conceptual background on ICA, see this scikit-learn tutorial. We begin as always by importing the necessary Python modules and loading some example data. Because ICA can be computationally intense, we’ll also crop the data to 60 seconds; and to save ourselves from repeatedly typing mne.preprocessing we’ll directly import a few functions…

related reading

• Independent component analysis - Wikipediaen.wikipedia.org
• Signal-space separation (SSS) and Maxwell filtering — MNE 1.13.2 documentationmne.tools
• Toward accessible, real-time brain decoding: Introducing ENIGMA | Alljoined Blogalljoined.com
• Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priorsmedarc-ai.github.io
• EEG Artifacts: Types, Detection, and Removal Techniques Bitbrainbitbrain.com
• Dimensionality reduction in neural data analysisxcorr.net
• Attribution Patching: Activation Patching At Industrial Scale - Neel Nandaneelnanda.io
• MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Datamedarc-ai.github.io
• [2305.18274] Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priorsarxiv.org
• AI to Interpret Your Mindjonathanxu.com
• Aman's AI Journal • CS229 • Principal Component Analysisaman.ai
• Principal Component Analysis (PCA) | Towards Data Sciencetowardsdatascience.com