On Robust Classification of Hemodynamic Signals for BCIs via Multiple Kernel ν-SVM

Berdakh Abibullaev; Jinung An

IROS 2016poster2 citations

On Robust Classification of Hemodynamic Signals for BCIs via Multiple Kernel ν-SVM

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.

BibTeX

@inproceedings{iros2016_onrobustclassifi,
  title = {On Robust Classification of Hemodynamic Signals for BCIs via Multiple Kernel ν-SVM},
  author = {Berdakh Abibullaev and Jinung An},
  booktitle = {IROS 2016},
  year = {2016}
}