Paper Info
Title
Discriminative canonical pattern matching for single-trial classification of ERP components.
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.
Year
DOI
Venue
2020
10.1109/TBME.2019.2958641
IEEE Transactions on Biomedical Engineering
Keywords
DocType
Volume
Electroencephalography,Band-pass filters,Electrodes,Visualization,Classification algorithms,Training,Electric potential
Journal
67
Issue
ISSN
Citations 
8
0018-9294
3
PageRank 
References 
Authors
0.39
0
6
Name
Order
Citations
PageRank
Xiaolin Xiao130.39
Minpeng Xu22717.17
Jing Jin353637.35
Yijun Wang430846.68
Tzyy-Ping Jung51410202.52
Dong Ming610551.47