Talk · IEEE/RSJ International Conference on Intelligent Robots and Systems · October 1, 2016
On Robust Classification of Hemodynamic Signals for BCIs via Multiple Kernel ν-SVM
Berdakh Abibullaev, Jinung An
Abstract
Near-Infrared spectroscopy (NIRS) is an emerging non-invasive brain computer interface (BCI) modality that measures changes in haemoglobin concentrations in the cortical tissue. To date most NIRS studies have used standard multiple subject/session dependent classifiers for neural signal decoding. Such approach is preferable to avoid large degree of variabilities in the acquired data that affects classifier generalization. This study presents a classification algorithm that maintains a good performance under the presence of variability in the NIRS data. It is based on ν- support vector machines and its extensions to a multiple kernel learning framework. Empirical evaluations have shown that through the proposed method one can improve the overall BCI decoding accuracy, and its robustness against the variability in neural data.
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