Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/15/5771 |
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| author | Mateo Tobón-Henao Andrés Álvarez-Meza Germán Castellanos-Domínguez |
| author_facet | Mateo Tobón-Henao Andrés Álvarez-Meza Germán Castellanos-Domínguez |
| author_sort | Mateo Tobón-Henao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T10:05:33Z |
| format | Article |
| id | doaj.art-84a32450406d47f2aed77e626fb5a028 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T10:05:33Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-84a32450406d47f2aed77e626fb5a0282023-12-01T23:10:19ZengMDPI AGSensors1424-82202022-08-012215577110.3390/s22155771Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor SkillsMateo Tobón-Henao0Andrés Álvarez-Meza1Germán Castellanos-Domínguez2Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, ColombiaThe 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.https://www.mdpi.com/1424-8220/22/15/5771Brain-Computer Interfaceelectroencephalographymotor imageryartifact removalfunctional connectivity |
| spellingShingle | Mateo Tobón-Henao Andrés Álvarez-Meza Germán Castellanos-Domínguez Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills Sensors Brain-Computer Interface electroencephalography motor imagery artifact removal functional connectivity |
| title | Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills |
| title_full | Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills |
| title_fullStr | Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills |
| title_full_unstemmed | Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills |
| title_short | Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills |
| title_sort | subject dependent artifact removal for enhancing motor imagery classifier performance under poor skills |
| topic | Brain-Computer Interface electroencephalography motor imagery artifact removal functional connectivity |
| url | https://www.mdpi.com/1424-8220/22/15/5771 |
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