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Automated artifact rejection algorithms harm P3 Speller brain-computer interface performance

Automated artifact rejection algorithms harm P3 Speller brain-computer interface performance Brain-Computer Interfaces (BCIs) have been used to restore communication and control to people with severe paralysis. However, noninvasive BCIs based on electroencephalogram (EEG) are particularly vulnerable to noise artifacts. These artifacts, including electro-oculogram (EOG), can be orders of magnitude larger than the signal to be detected. Many automated methods have been proposed to remove EOG and other artifacts from EEG recordings, most based on blind source separation. This work presents a performance comparison of ten different automated artifact removal methods. Unfortunately, all tested methods substantially and significantly reduced P3 Speller BCI performance, and all methods were more likely to reduce performance than increase it. The least harmful methods were titled SOBI, JADER, and EFICA, but even these methods caused an average of approximately ten percentage points drop in BCI accuracy. Possible mechanistic causes for this empirical performance reduction are proposed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Automated artifact rejection algorithms harm P3 Speller brain-computer interface performance

Automated artifact rejection algorithms harm P3 Speller brain-computer interface performance

Abstract

Brain-Computer Interfaces (BCIs) have been used to restore communication and control to people with severe paralysis. However, noninvasive BCIs based on electroencephalogram (EEG) are particularly vulnerable to noise artifacts. These artifacts, including electro-oculogram (EOG), can be orders of magnitude larger than the signal to be detected. Many automated methods have been proposed to remove EOG and other artifacts from EEG recordings, most based on blind source separation. This work...
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Publisher
Taylor & Francis
Copyright
© 2020 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2020.1734401
Publisher site
See Article on Publisher Site

Abstract

Brain-Computer Interfaces (BCIs) have been used to restore communication and control to people with severe paralysis. However, noninvasive BCIs based on electroencephalogram (EEG) are particularly vulnerable to noise artifacts. These artifacts, including electro-oculogram (EOG), can be orders of magnitude larger than the signal to be detected. Many automated methods have been proposed to remove EOG and other artifacts from EEG recordings, most based on blind source separation. This work presents a performance comparison of ten different automated artifact removal methods. Unfortunately, all tested methods substantially and significantly reduced P3 Speller BCI performance, and all methods were more likely to reduce performance than increase it. The least harmful methods were titled SOBI, JADER, and EFICA, but even these methods caused an average of approximately ten percentage points drop in BCI accuracy. Possible mechanistic causes for this empirical performance reduction are proposed.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Oct 2, 2019

Keywords: Brain-computer interfaces; P300 Speller; artifacts rejection; physiological signals; signal processing

References