Title
Brain--computer interface (BCI): is it strictly necessary to use random sequences in visual spellers?
Abstract
The P300 speller is a standard paradigm for brain--computer interfacing (BCI) based on electroencephalography (EEG). It exploits the fact that the user's selective attention to a target stimulus among a random sequence of stimuli enhances the magnitude of the P300 evoked potential. The present study questions the necessity of using random sequences of stimulation. In two types of experimental runs, subjects attended to a target stimulus while the stimuli, four in total, were each intensified twelve times, in either random order or deterministic order. The 32-channel EEG data were analyzed offline using linear discriminant analysis (LDA). Similar classification accuracies of 95.3% and 93.2% were obtained for the random and deterministic runs, respectively, using the data associated with 3 sequences of stimulation. Furthermore, using a montage of 5 posterior electrodes, the two paradigms attained identical accuracy of 92.4%. These results suggest that: (a) the use of random sequences is not necessary for effective BCI performance; and (b) deterministic sequences can be used in some BCI speller applications.
Year
DOI
Venue
2012
10.1145/2350046.2350071
APCHI
Keywords
Field
DocType
target stimulus,deterministic run,32-channel eeg data,deterministic order,p300 evoked potential,random order,visual speller,effective bci performance,computer interface,deterministic sequence,bci speller application,random sequence,selective attention,brain computer interface,oddball paradigm,electroencephalography
Pattern recognition,Computer science,Brain–computer interface,Random sequence,Oddball paradigm,Interfacing,Speech recognition,Evoked potential,Artificial intelligence,Stimulus (physiology),Linear discriminant analysis,Electroencephalography
Conference
Citations 
PageRank 
References 
0
0.34
3
Authors
4
Name
Order
Citations
PageRank
Manson C.-M. Fong191.44
James William Minett200.34
T Blu32574259.70
William Shi-Yuan Wang400.68