Effect of Independent Component Artifact Rejection on EEG-Based Auditory Attention Decoding
(2024) 32nd European Signal Processing Conference, EUSIPCO 2024 p.877-881- Abstract
Effective preprocessing of electroencephalography (EEG) data is fundamental for deriving meaningful insights. Independent component analysis (ICA) serves as an important step in this process by aiming to eliminate undesirable artifacts from EEG data. However, the decision on which and how many components to be removed remains somewhat arbitrary, despite the availability of both automatic and manual artifact rejection methods based on ICA. This study investigates the influence of different ICA-based artifact rejection strategies on EEG-based auditory attention decoding (AAD) analysis. We employ multiple ICA-based artifact rejection approaches, ranging from manual to automatic versions, and assess their effects on conventional AAD... (More)
Effective preprocessing of electroencephalography (EEG) data is fundamental for deriving meaningful insights. Independent component analysis (ICA) serves as an important step in this process by aiming to eliminate undesirable artifacts from EEG data. However, the decision on which and how many components to be removed remains somewhat arbitrary, despite the availability of both automatic and manual artifact rejection methods based on ICA. This study investigates the influence of different ICA-based artifact rejection strategies on EEG-based auditory attention decoding (AAD) analysis. We employ multiple ICA-based artifact rejection approaches, ranging from manual to automatic versions, and assess their effects on conventional AAD methods. The comparison aims to uncover potential variations in analysis results due to different artifact rejection choices within pipelines, and whether such variations differ across different AAD methods. Although our study finds no large difference in performance of linear AAD models between artifact rejection methods, two exeptions were found. When predicting EEG responses, the manual artifact rejection method appeared to perform better in frontal channel groups. Conversely, when reconstructing speech envelopes from EEG, not using artifact rejection outperformed other approaches.
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- author
- Keding, Oskar LU ; Wilroth, Johanna ; Skoglund, Martin A. and Alickovic, Emina
- organization
- publishing date
- 2024
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Artifact Rejection, Attention, Electroencephalography, Hearing, Independent Component Analysis
- host publication
- 32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings
- pages
- 5 pages
- publisher
- European Signal Processing Conference, EUSIPCO
- conference name
- 32nd European Signal Processing Conference, EUSIPCO 2024
- conference location
- Lyon, France
- conference dates
- 2024-08-26 - 2024-08-30
- external identifiers
-
- scopus:85208439184
- ISBN
- 9789464593617
- DOI
- 10.23919/eusipco63174.2024.10715429
- language
- English
- LU publication?
- yes
- id
- 5af915e3-ba94-4067-ab7b-05aaac6788ad
- date added to LUP
- 2025-02-18 09:50:42
- date last changed
- 2025-04-04 14:18:18
@inproceedings{5af915e3-ba94-4067-ab7b-05aaac6788ad,
abstract = {{<p>Effective preprocessing of electroencephalography (EEG) data is fundamental for deriving meaningful insights. Independent component analysis (ICA) serves as an important step in this process by aiming to eliminate undesirable artifacts from EEG data. However, the decision on which and how many components to be removed remains somewhat arbitrary, despite the availability of both automatic and manual artifact rejection methods based on ICA. This study investigates the influence of different ICA-based artifact rejection strategies on EEG-based auditory attention decoding (AAD) analysis. We employ multiple ICA-based artifact rejection approaches, ranging from manual to automatic versions, and assess their effects on conventional AAD methods. The comparison aims to uncover potential variations in analysis results due to different artifact rejection choices within pipelines, and whether such variations differ across different AAD methods. Although our study finds no large difference in performance of linear AAD models between artifact rejection methods, two exeptions were found. When predicting EEG responses, the manual artifact rejection method appeared to perform better in frontal channel groups. Conversely, when reconstructing speech envelopes from EEG, not using artifact rejection outperformed other approaches.</p>}},
author = {{Keding, Oskar and Wilroth, Johanna and Skoglund, Martin A. and Alickovic, Emina}},
booktitle = {{32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings}},
isbn = {{9789464593617}},
keywords = {{Artifact Rejection; Attention; Electroencephalography; Hearing; Independent Component Analysis}},
language = {{eng}},
pages = {{877--881}},
publisher = {{European Signal Processing Conference, EUSIPCO}},
title = {{Effect of Independent Component Artifact Rejection on EEG-Based Auditory Attention Decoding}},
url = {{http://dx.doi.org/10.23919/eusipco63174.2024.10715429}},
doi = {{10.23919/eusipco63174.2024.10715429}},
year = {{2024}},
}