R Package: DA4BCI
Published:
Package: DA4BCI (Data Augmentation for Brain-Computer Interface)
Version: 3.1.0
Maintainer: Yiming Shen
Overview
DA4BCI is an R package designed to address the data scarcity problem in BCI research. It implements advanced algorithms to generate artificial EEG data, specifically optimized for covariance-based decoding pipelines.
Key Features
- Geometric Data Generation: Unlike traditional noise injection, this package generates synthetic data directly on the Riemannian manifold of Symmetric Positive Definite (SPD) matrices.
- Calibration Optimization: Specifically designed to improve the calibration of BCI classifiers when training data is limited.
- Algorithm Support: Provides helper functions compatible with Riemannian Minimum Distance to Mean (MDM) and Tangent Space classifiers.
Installation & Usage
You can install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("Yiming-S/DA4BCI")Example: Generating Artificial Covariance Matrices
library(DA4BCI)
# Load sample EEG covariance data
data("sample_covariances")
# Generate 100 artificial trials using Riemannian interpolation
synthetic_data <- generate_riemannian_data(cov_matrices = sample_covariances,
n_samples = 100,
method = "geodesic")