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Discriminative Canonical Pattern Matching for Single-Trial Classification of ERP Components.

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Title: Discriminative Canonical Pattern Matching for Single-Trial Classification of ERP Components.
Authors: Xiao, Xiaolin1, Xu, Minpeng1, Jin, Jing2, Wang, Yijun3 wangyj@semi.ac.cn, Jung, Tzyy-Ping4, Ming, Dong5 richardming@tju.edu.cn
Source: IEEE Transactions on Biomedical Engineering. Aug2020, Vol. 67 Issue 8, p2266-2275. 10p.
Subjects: Pattern matching, Visual evoked potentials, Fisher discriminant analysis, Brain-computer interfaces, Classification algorithms, Decoding algorithms, Electroencephalography
Abstract: Event-related potentials (ERPs) are one of the most popular control signals for brain–computer interfaces (BCIs). However, they are very weak and sensitive to the experimental settings including paradigms, stimulation parameters and even surrounding environments, resulting in a diversity of ERP patterns across different BCI experiments. It's still a challenge to develop a general decoding algorithm that can adapt to the ERP diversities of different BCI datasets with small training sets. This study compared a recently developed algorithm, i.e., discriminative canonical pattern matching (DCPM), with seven ERP-BCI classification methods, i.e., linear discriminant analysis (LDA), stepwise LDA, bayesian LDA, shrinkage LDA, spatial-temporal discriminant analysis (STDA), xDAWN and EEGNet for the single-trial classification of two private EEG datasets and three public EEG datasets with small training sets. The feature ERPs of the five datasets included P300, motion visual evoked potential (mVEP), and miniature asymmetric visual evoked potential (aVEP). Study results showed that the DCPM outperformed other classifiers for all of the tested datasets, suggesting the DCPM is a robust classification algorithm for assessing a wide range of ERP components. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Biomedical Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Pattern+matching%22">Pattern matching</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+evoked+potentials%22">Visual evoked potentials</searchLink><br /><searchLink fieldCode="DE" term="%22Fisher+discriminant+analysis%22">Fisher discriminant analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Brain-computer+interfaces%22">Brain-computer interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Decoding+algorithms%22">Decoding algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink>
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  Data: Event-related potentials (ERPs) are one of the most popular control signals for brain–computer interfaces (BCIs). However, they are very weak and sensitive to the experimental settings including paradigms, stimulation parameters and even surrounding environments, resulting in a diversity of ERP patterns across different BCI experiments. It's still a challenge to develop a general decoding algorithm that can adapt to the ERP diversities of different BCI datasets with small training sets. This study compared a recently developed algorithm, i.e., discriminative canonical pattern matching (DCPM), with seven ERP-BCI classification methods, i.e., linear discriminant analysis (LDA), stepwise LDA, bayesian LDA, shrinkage LDA, spatial-temporal discriminant analysis (STDA), xDAWN and EEGNet for the single-trial classification of two private EEG datasets and three public EEG datasets with small training sets. The feature ERPs of the five datasets included P300, motion visual evoked potential (mVEP), and miniature asymmetric visual evoked potential (aVEP). Study results showed that the DCPM outperformed other classifiers for all of the tested datasets, suggesting the DCPM is a robust classification algorithm for assessing a wide range of ERP components. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of IEEE Transactions on Biomedical Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TBME.2019.2958641
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      – TitleFull: Discriminative Canonical Pattern Matching for Single-Trial Classification of ERP Components.
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              Text: Aug2020
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