NDLI: Optimization of BCI Speller Based on P300 Potential

Content Provider	IEEE Xplore Digital Library
Author	Thulasidas, M. Guan, C.
Copyright Year	2005
Description	Author affiliation: Neural Signal Process. Lab, Institute for Infocomm Res. (Thulasidas, M.; Guan, C.)
Abstract	We report our studies on a brain computer interface (BCI) speller application with an aim to optimize its performance and usability. We study the dependence of the spelling accuracy as a function of (a) the number of visual stimuli (repetitions) presented to the user, (b) the P300 segment length used, (c) the number of channels used, and (d) the amount of data used in training, in terms of the number of characters and repetitions. Reducing the number of repetitions results in a direct reduction of the time needed to spell a character, while minimizing the number of channels translates to shorter subject preparation time and thus improves the usability of the system. The usability is further enhanced by decreasing the training required, while maintaining the accuracy. We show that very high accuracies of the order of 99% can be achieved with a short training session of less than 10 minutes using only about 10 channels. The high accuracies, short training and preparation time requirements along with real-time performance make this BCI speller a viable communication tool for severely disabled individuals, who have no other means to communicate with the external world
Starting Page	5396
Ending Page	5399
File Size	187374
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1615702
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Usability Brain computer interfaces Electroencephalography Support vector machines Support vector machine classification Signal processing Application software Communication channels Noise level Conductivity
Content Type	Text
Resource Type	Article

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