Blog

Subsetting vectors in R

R

Basics

Extracting one or more elements from a vector is a fundamental and important operation in any data analysis pipeline. In this post, I showcase various options for creating subsets of vectors.

Subsetting data frames in R

R

Basics

Selecting rows and columns from a data frame are basic data manipulation operations. In this post, I show several options for creating subsets of data frames in R, and I also point out important differences between classic data.frame and Tidyverse tibble objects.

Warming stripes with R

R

Visualization

Warming stripes are a popular way to represent average temperature changes in a particular location over time. In this post, I show how to generate these visualizations using R.

Removing Google Fonts from a Quarto website

Quarto

Web

Many website frameworks and themes embed Google Fonts, which is very convenient. However, contacting third-party servers in the background without acquiring explicit consent might violate data privacy laws. In addition, using local fonts (or fonts available directly on a website) is often faster than retrieving from an external server. In this post, I show how to disable Google Fonts and use fonts stored on your own server for websites generated with Quarto and its built-in themes.

Whitening with PCA and ZCA

Python

EEG

ICA

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).

Removing eye activity from EEG via ICA

Python

MNE

EEG

EOG

Artifacts

ICA

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).

Visualizing EEG data

Python

MNE

EEG

Artifacts

After importing 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.

Importing EEG data

Python

MNE

EEG

Python is an extremely popular programming language, and the scientific Python community has created a striving ecosystem of neuroscience tools. Among these, MNE is the most popular EEG/MEG package, which offers almost anything required in an EEG processing pipeline. In this post, I show how to import EEG data sets and how to view and edit associated meta data.

Removing eye activity from EEG via regression

Python

MNE

EEG

EOG

Artifacts

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.

Setting up Python for EEG analysis

Python

EEG

Basics

Installing Python is pretty straightforward. In this post, I describe how to set up Python for EEG analysis on Windows, macOS, and Linux.