R Package: eegwhiten
Published:
Package: eegwhiten (EEG Signal Whitening Toolkit)
Version: 1.1.0
Maintainer: Yiming Shen
Overview
eegwhiten provides a robust suite of methods for whitening and standardizing EEG signals. Whitening is a critical preprocessing step for decorrelating signal channels, which is essential for effective covariance estimation and subsequent classification in BCI pipelines.
Key Methodologies
The package implements the following whitening transformations:
- PCA Whitening: Principal Component Analysis based whitening to maximize variance in orthogonal directions.
- ZCA Whitening (Mahalanobis): Zero-phase Component Analysis, preserving the spatial arrangement of the original sensors while decorrelating them.
- Cholesky Decomposition: Uses the Cholesky factor of the inverse covariance matrix for efficient whitening.
- Standardization: Basic signal scaling and centering utilities.
Installation & Usage
You can install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("Yiming-S/eegwhiten")Quick Start
library(eegwhiten)
# Apply ZCA whitening to an EEG epoch
# X is an [n_channels x n_timepoints] matrix
X_whitened <- eeg_whiten(X, method = "ZCA")
# Check correlation matrix (should be Identity)
print(round(cor(t(X_whitened)), 2))