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.
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Ahi, S.T., Yoshimura, N., Kambara, H., Koike, Y. (2010). Utilizing Fuzzy-SVM and a Subject Database to Reduce the Calibration Time of P300-Based BCI. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_1
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DOI: https://doi.org/10.1007/978-3-642-17534-3_1
Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-642-17534-3
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