Utilizing fuzzy-SVM and a subject database to reduce the calibration time of P300-based BCI

Authors:
Sercan Taha Ahi;Natsue Yoshimura;Hiroyuki Kambara;Yasuharu Koike
Affiliations:
Department of Computational Intelligence and System Science, Tokyo Institute of Technology, Yokohama, Japan;Precision and Intelligence Laboratory, Tokyo Institute of Technology, Yokohama, Japan;Precision and Intelligence Laboratory, Tokyo Institute of Technology, Yokohama, Japan;Solution Science Research Laboratory, Tokyo Institute of Technology, Yokohama, Japan
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

Citing 4
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Fuzzy support vector machines

IEEE Transactions on Neural Networks

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Abstract

Current Brain-Computer Interfaces (BCI) suffer the requirement of a subject-specific calibration process due to variations in EEG responses across different subjects. Additionally, the duration of the calibration process should be long enough to sufficiently sample high dimensional feature spaces. In this study, we proposed a method based on Fuzzy Support Vector Machines (Fuzzy-SVM) to address both issues for P300-based BCI. We conducted P300 speller experiments on 18 subjects, and formed a subject-database using a leave-one-out approach. By computing weight values for the data samples obtained from each subject, and by incorporating those values into the Fuzzy-SVM algorithm, we achieved to obtain an average accuracy of 80% with only 4 training letters. Conventional subject-specific calibration approach, on the other hand, needed 12 training letters to provide the same performance.