ID | 116331 |
Title Alternative | SSL for Auditory ERP-Based BCI
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Author |
Ogino, Mikito
Keio University|Dentsu ScienceJam
Kanoga, Suguru
National Institute of Advanced Industrial Science and Technology
Ito, Shin-ichi
Tokushima University
Tokushima University Educator and Researcher Directory
KAKEN Search Researchers
Mitsukura, Yasue
Keio University
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Keywords | Auditory stimuli
brain-computer interface
event-related potential
semi-supervised learning
P300
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Content Type |
Journal Article
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Description | A brain–computer interface (BCI) is a communication tool that analyzes neural activity and relays the translated commands to carry out actions. In recent years, semi-supervised learning (SSL) has attracted attention for visual event-related potential (ERP)-based BCIs and motor-imagery BCIs as an effective technique that can adapt to the variations in patterns among subjects and trials. The applications of the SSL techniques are expected to improve the performance of auditory ERP-based BCIs as well. However, there is no conclusive evidence supporting the positive effect of SSL techniques on auditory ERP-based BCIs. If the positive effect could be verified, it will be helpful for the BCI community. In this study, we assessed the effects of SSL techniques on two public auditory BCI datasets—AMUSE and PASS2D—using the following machine learning algorithms: step-wise linear discriminant analysis, shrinkage linear discriminant analysis, spatial temporal discriminant analysis, and least-squares support vector machine. These backbone classifiers were firstly trained by labeled data and incrementally updated by unlabeled data in every trial of testing data based on SSL approach. Although a few data of the datasets were negatively affected, most data were apparently improved by SSL in all cases. The overall accuracy was logarithmically increased with every additional unlabeled data. This study supports the positive effect of SSL techniques and encourages future researchers to apply them to auditory ERP-based BCIs.
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Journal Title |
IEEE Access
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ISSN | 21693536
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Publisher | IEEE
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Volume | 9
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Start Page | 47008
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End Page | 47023
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Published Date | 2021-03-19
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Rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
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language |
eng
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departments |
Science and Technology
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