Paper Info
Title
A Comparison Of Classification Methods For Recognizing Single-Trial P300 In Brain-Computer Interfaces
Abstract
P300s are one of the most popular and robust control signals for brain-computer interfaces (BCIs). Fast classifying P300s is vital for the good performance of P300-based BCIs. However, due to noisy background electroencephalography (EEG) environments, current P300-based BCI systems need to collect multiple trials for a reliable output, which is inefficient. This study compared a recently developed algorithm, i.e. discriminative canonical pattern matching (DCPM), with five traditional classification methods, i.e. linear discriminant analysis (LDA), stepwise LDA, Bayesian LDA, shrinkage LDA and spatial-temporal discriminant analysis (STDA), for the detection of single-trial P300s. Eight subjects participated in the classical P300-speller experiments. Study results showed that the DCPM significantly outperformed the other traditional methods in single-trial P300 classification even with small training samples, suggesting the DCPM is a promising classification algorithm for the P300-based BCI.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8857521
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Keywords
Field
DocType
Brain-computer interface (BCI), single-trial, P300, discriminative canonical pattern matching (DCPM)
Computer vision,Pattern recognition,Visualization,Computer science,Brain–computer interface,Artificial intelligence,Linear discriminant analysis,Statistical classification,Robust control,Pattern matching,Discriminative model,Bayesian probability
Conference
Volume
ISSN
Citations 
2019
1557-170X
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Xiaolin Xiao1366.57
Minpeng Xu22717.17
Yijun Wang330846.68
Tzyy-Ping Jung41410202.52
Dong Ming510551.47