The application of Riemannian geometry in the decoding of brain-computer interfaces (BCIs)
has swiftly garnered attention because of its straightforwardness, precision, and resilience …

Matrix-variate distributions can intuitively model the dependence structure of matrix-valued
observations that arise in applications with multivariate time series, spatio-temporal, or …

The issue of variable selection has been widely investigated for different purposes, such as
clustering, classification or function approximation becoming the focus of many research …

Over the past two decades, matrix-based or bilinear discriminant analysis (BLDA) methods
have received much attention. However, it has been reported that the traditional vector …

Fisher linear discriminant analysis (LDA) involves within-class and between-class
covariance matrices. For 2-D data such as images, regularized LDA (RLDA) can improve …

In brain‐computer interface (BCI), feature extraction is the key to the accuracy of recognition.
There is important local structural information in the EEG signals, which is effective for …

Principal component analysis (PCA) is a popular dimension reduction technique for vector
data. Factored PCA (FPCA) is a probabilistic extension of PCA for matrix data, which can …

Multivariate time series (MTS) is a kind of matrix data, typically consisting of multiple
variables measured at multiple time points. Due to the high dimensionality of MTS data …

This paper proposes a novel method for extraction of discriminant spatio-spectral EEG
features in motor imagery brain-computer interfaces. Considering a heteroscedastic binary …

Linear discriminant analysis (LDA) is a commonly-used feature extraction technique. For
matrix-variate data such as spatio-spectral electroencephalogram (EEG), matrix-variate LDA …