@article{ref1,
title="Subject-dependent artifact removal for enhancing motor imagery classifier performance under poor skills",
journal="Sensors (Basel)",
year="2022",
author="Tobón-Henao, Mateo and Álvarez-Meza, Andrés and Castellanos-Domínguez, Germán",
volume="22",
number="15",
pages="e5771-e5771",
abstract="The Electroencephalography (EEG)-based motor imagery (MI) paradigm is one of the most studied technologies for Brain-Computer Interface (BCI) development. Still, the low Signal-to-Noise Ratio (SNR) poses a challenge when constructing EEG-based BCI systems. Moreover, the non-stationary and nonlinear signal issues, the low-spatial data resolution, and the inter- and intra-subject variability hamper the extraction of discriminant features. Indeed, subjects with poor motor skills have difficulties in practicing MI tasks against low SNR scenarios. Here, we propose a subject-dependent preprocessing approach that includes the well-known Surface Laplacian Filtering and Independent Component Analysis algorithms to remove signal artifacts based on the MI performance. In addition, power- and phase-based functional connectivity measures are studied to extract relevant and interpretable patterns and identify subjects of inefficency. As a result, our proposal, Subject-dependent Artifact Removal (SD-AR), improves the MI classification performance in subjects with poor motor skills. Consequently, electrooculography and volume-conduction EEG artifacts are mitigated within a functional connectivity feature-extraction strategy, which favors the classification performance of a straightforward linear classifier.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22155771",
url="http://dx.doi.org/10.3390/s22155771"
}