Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings
In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Universidad Distrital Francisco José de Caldas
2023-01-01
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| Series: | Revista Científica |
| Subjects: | |
| Online Access: | https://200.69.103.50/index.php/revcie/article/view/19068 |
| _version_ | 1797946381599506432 |
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| author | Luisa-María Zapata-Saldarriaga Angie-Dahiana Vargas-Serna Jesica Gil-Gutiérrez Yorguin-Jose Mantilla-Ramos John-Fredy Ochoa-Gómez |
| author_facet | Luisa-María Zapata-Saldarriaga Angie-Dahiana Vargas-Serna Jesica Gil-Gutiérrez Yorguin-Jose Mantilla-Ramos John-Fredy Ochoa-Gómez |
| author_sort | Luisa-María Zapata-Saldarriaga |
| collection | DOAJ |
| description |
In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built.
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| first_indexed | 2024-04-10T21:11:13Z |
| format | Article |
| id | doaj.art-a09f207d8de547d2904d2389e98daa65 |
| institution | Directory Open Access Journal |
| issn | 0124-2253 2344-8350 |
| language | English |
| last_indexed | 2024-04-10T21:11:13Z |
| publishDate | 2023-01-01 |
| publisher | Universidad Distrital Francisco José de Caldas |
| record_format | Article |
| series | Revista Científica |
| spelling | doaj.art-a09f207d8de547d2904d2389e98daa652023-01-20T21:14:13ZengUniversidad Distrital Francisco José de CaldasRevista Científica0124-22532344-83502023-01-01461Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG RecordingsLuisa-María Zapata-Saldarriaga0Angie-Dahiana Vargas-Serna1Jesica Gil-Gutiérrez2Yorguin-Jose Mantilla-Ramos3John-Fredy Ochoa-Gómez4Universidad de AntioquiaUniversidad de AntioquiaUniversidad de AntioquiaUniversidad de AntioquiaUniversidad de Antioquia In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built. https://200.69.103.50/index.php/revcie/article/view/19068ArtifactsAlzheimeralphaelectroencephalographyeffect sizeE280A |
| spellingShingle | Luisa-María Zapata-Saldarriaga Angie-Dahiana Vargas-Serna Jesica Gil-Gutiérrez Yorguin-Jose Mantilla-Ramos John-Fredy Ochoa-Gómez Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings Revista Científica Artifacts Alzheimer alpha electroencephalography effect size E280A |
| title | Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings |
| title_full | Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings |
| title_fullStr | Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings |
| title_full_unstemmed | Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings |
| title_short | Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings |
| title_sort | evaluation of strategies based on wavelet ica and iclabel for artifact correction in eeg recordings |
| topic | Artifacts Alzheimer alpha electroencephalography effect size E280A |
| url | https://200.69.103.50/index.php/revcie/article/view/19068 |
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