Paper Info
Title
Feature enhancement of P300 based brain computer interface through spatially-constrained ICA
Abstract
The P300 word speller is one of the important BC' applications which detects real-time P300 waveforms and translates them into letters (and then words) within a particular BC' paradigm. However due to the poor SNR of EEG, as well as the presence of other artifacts, the identification accuracy is still not high enough for real-world application. This paper presents two slightly different 'CA approaches to improve character classification performance based on 'CA. When compared with the classification results obtained from the data, the results using these approaches show distinct improvement. Furthermore, the results indicate that it is possible to reduce the number of epochs required to perform stimulus locked averages, whilst still maintaining good performance measures. This has the potential of speeding up the word speller and has further implications for the use on similar ERP based systems.
Year
DOI
Venue
2012
10.1109/VECIMS.2012.6273189
VECIMS
Keywords
Field
DocType
stimulus locked averages,p300,event-related potential,bci,electroencephalogram,p300 waveforms,electroencephalography,brain-computer interfaces,erp based systems,spatially-constrained ica,medical signal processing,p300 feature enhancement,independent component analysis,character classification performance,p300 word speller,signal classification,constraint,brain computer interface,eeg,ica,support vector machines,brain computer interfaces,accuracy,electric potential,event related potential
Pattern recognition,Computer science,Support vector machine,Brain–computer interface,Event-related potential,Speech recognition,Signal classification,Independent component analysis,Artificial intelligence,Electroencephalography
Conference
Volume
Issue
ISSN
null
null
1944-9429
ISBN
Citations 
PageRank 
978-1-4577-1758-1
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Suogang Wang1121.42
Christopher J. James220621.93