Paper Info
Title
Improving the performance of P300-based brain-computer interface through subspace-based filtering
Abstract
The detection of the presence of the P300 in the electroencephalogram (EEG) is a challenging issue in P300-based brain-computer interface (BCI). The P300-based developed in this work allows a subject to communicate 1 of 36 symbols presented on a 6x6 matrix. When a target symbol is seen by the subject, unique event-related potential (ERP) characterized by P300 is elicited. Thus, in P300 speller, accurate detection of such distinct ERP provides faster and reliable communication. In this paper, a subspace-based spatial filter was employed to enhance the detection of the presence of the P300 in the EEG. The subspace-based filter was designed by maximizing the ratio between the brain signals synchronized with the target stimulus and that with the nontarget stimulus. The processing method was evaluated offline and online on the data obtained from five subjects. The results of offline studies showed that the average accuracies of 97.5% and 90.5% were achieved in P300 detection and character recognition, respectively. The average communication rate achieved was 17.13bits/min with an average accuracy of 89.5% using 10 electrodes during online experiments.
Year
DOI
Venue
2013
10.1016/j.neucom.2013.05.001
Neurocomputing
Keywords
Field
DocType
nontarget stimulus,p300 detection,average accuracy,distinct erp,average communication rate,online experiment,offline study,accurate detection,p300 speller,p300-based brain-computer interface,brain computer interface
Character recognition,Pattern recognition,Subspace topology,Computer science,Brain–computer interface,Filter (signal processing),Speech recognition,Artificial intelligence,Stimulus (physiology),Machine learning,Electroencephalography,Spatial filter
Journal
Volume
ISSN
Citations 
121,
0925-2312
2
PageRank 
References 
Authors
0.40
19
2
Name
Order
Citations
PageRank
Yalda Shahriari131.80
Abbas Erfanian2587.35