NDLI: Improving the Performance of P300-Speller with Familiar Face Paradigm Using Support Vector Machine Ensemble

Content Provider	IEEE Xplore Digital Library
Author	Qi Li Jian Li Shuai Liu Yang Wu
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Changchun Univ. of Sci. & Technol., Changchun, China (Qi Li; Jian Li; Shuai Liu; Yang Wu)
Abstract	P300-speller is a communication style based on Brain-computer interface (BCI) which allows users to input characters by electroencephalography (EEG) signals. In the past few years, there are various studies on P300-speller paradigm and classification algorithm. However, the accuracy and bit rates are not yet satisfied for our daily life. In order to improve the performance of the P300-speller, we designed an experiment in which support vector machine ensemble for P300-speller with familiar face paradigm was used. Seventeen subjects participated in the experiment and achieved a good classification accuracy. The results showed that support vector machine ensemble enhanced the performance of P300-speller with familiar face paradigm.
Starting Page	606
Ending Page	610
File Size	181944
Page Count	5
File Format	PDF
e-ISBN	9781479918430
DOI	10.1109/ICNISC.2015.75
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines SVM Ensemble Accuracy Familiar Face Paradigm Training data P300 Feature extraction BCI Electroencephalography SVM Face Testing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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