EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
This work addresses EEG-based reaction time estimation for brain-computer interface applications, representing an incremental advancement by extending Riemannian geometry from classification to regression tasks.
The paper tackled the problem of estimating user reaction time from EEG signals by applying Riemannian geometry features to BCI regression for the first time, resulting in a reduction of root mean square estimation error by 4.30-8.30% and an increase in correlation coefficient by 6.59-11.13% compared to traditional methods.
Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for Electroencephalogram (EEG) based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.