Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills

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.

Advancements in Image Feature-Based Classification of Motor Imagery EEG Data: A Comprehensive Review

“…EEG signals of subjects with poor MI execution performance should also be improved. To solve this problem, MI performance-based artefacts under poor skill must be removed as in Tobón-Henao et al [36]. The large number of channels affects both performance and practical applications.…”

KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification

“…In other words, ERP gather time-frequency information about different brain areas [ 8 ]. However, it is essential to note that MI’s Signal-to-Noise Ratio (SNR) can be significantly affected by other background brain processes and artifacts [ 9 ].…”

Kernel-Based Regularized EEGNet Using Centered Alignment and Gaussian Connectivity for Motor Imagery Discrimination

“…In contrast, SL aspires to remove the common brain activity of neighboring sensors due to the volume conduction effect, which improves local topographical features, facilitates sensor-level connectivity analysis, and helps to enhance the SNR [29]. Despite the effectiveness of these methods, applying them to all subjects regardless of the individual noise level can be detrimental to subjects with clean EEG [30].…”