Minimizing inter-subject variability in fNIRS-based brain-computer interfaces via multiple-kernel support vector learning.
Brain signal variation across different subjects and sessions significantly impairs the accuracy of most brain-computer interface (BCI) systems. Herein, we present a classification algorithm that minimizes such variation, using linear programming support-vector machines (LP-SVM) and their extension to multiple kernel learning methods. The minimization is based on the decision boundaries formed in classifiers' feature spaces and their relation to BCI variation. Specifically, we estimate subject/session-invariant features in the reproducing kernel Hilbert spaces (RKHS) induced with Gaussian kernels. The idea is to construct multiple subject/session-dependent RKHS and to perform classification with LP-SVMs. To evaluate the performance of the algorithm, we applied it to oxy-hemoglobin data sets acquired from eight sessions and seven subjects as they performed two different mental tasks. Results show that our classifiers maintain good performance when applied to random patterns across varying sessions/subjects.
Department of Robotics, School of Science and Technology, Nazarbayev University, Astana 010000, Republic of Kazakhstan.
Berdakh
B
Abibullaev
Jinung
J
An
Sang-Hyeon
SH
Jin
Seung Hyun
SH
Lee
Jeon Il
JI
Moon
D000328:Adult; D000465:Algorithms; D062207:Brain-Computer Interfaces; D006801:Humans; D013055:Spectrophotometry, Infrared / Q000379:methods*; D060388:Support Vector Machine
Brain–computer interfaces; Functional near-infrared spectroscopy; Inter-subject variability; Multiple kernel learning; RKHS; Support vector machines