Despite its short history, the use of Riemannian geometry in brain-computer interface (BCI)
decoding is currently attracting increasing attention, due to accumulating documentation of …

Describing the color and textural information of a person image is one of the most crucial
aspects of person re-identification. In this paper, we present a novel descriptor based on a …

Objective. In this paper, we investigate the suitability of imagined speech for brain–computer
interface (BCI) applications. Approach. A novel method based on covariance matrix …

By characterizing each image set as a nonsingular covariance matrix on the symmetric
positive definite (SPD) manifold, the approaches of visual content classification with image …

Objective: This paper presents a Transfer Learning approach for dealing with the statistical
variability of electroencephalographic (EEG) signals recorded on different sessions and/or …

This paper proposes a new neural network based on SPD manifold learning for skeleton-
based hand gesture recognition. Given the stream of hand's joint positions, our approach …

When dealing with electro or magnetoencephalography records, many supervised
prediction tasks are solved by working with covariance matrices to summarize the signals …

Emotion plays a vital role in human daily life, and EEG signals are widely used in emotion
recognition. Due to individual variability, training a generic emotion recognition model …

In this paper, we revive the use of old-fashioned handcrafted video representations for
action recognition and put new life into these techniques via a CNN-based hallucination …

By representing each image set as a nonsingular covariance matrix on the symmetric
positive definite (SPD) manifold, visual classification with image sets has attracted much …