Whitening (or sphering) is an important preprocessing step prior to performing independent component analysis (ICA) on EEG/MEG data. In this post, I explain the intuition behind whitening and illustrate the difference between two popular whitening methods – PCA (principal component analysis) and ZCA (zero-phase component analysis).
EEG signals often contain eye activity (movement and/or blinks), which usually needs to be removed before performing EEG analysis. In this post, I show how to remove such ocular artifacts using independent component analysis (ICA).
After loading EEG data, it is usually helpful to visualize the raw EEG traces. Despite the availability of numerous automated artifact removal or reduction techniques, manual inspection remains important (often in combination with automated methods) to obtain clean data. In this post, I show how to visualize an EEG data set and how to interactively mark segments containing artifacts.
Python is an extremely popular programming language for data analysis in general. In addition, the scientific Python community has created a striving ecosystem of neuroscience tools. A popular EEG/MEG toolbox is MNE, which offers almost anything required in an EEG processing pipeline. In this post, I show how to load EEG data sets and how to view and edit associated meta information.
EEG signals often contain eye activity (movement and/or blinks), which usually needs to be removed before performing EEG analysis. In this post, I show how to get rid of ocular artifacts using a regression-based approach.
Installing Python is usually pretty straightforward, but there are some gotchas when setting up Python for scientific computing. In this post, I describe pros and cons of various installation methods (and recommend one particular option for EEG analysis).