Paper Info
Title
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.
Year
Venue
Field
2016
IROS
Kernel (linear algebra),Pattern recognition,Computer science,Support vector machine,Brain–computer interface,Multiple kernel learning,Robustness (computer science),Artificial intelligence,Decoding methods,Classifier (linguistics),Principal component analysis,Machine learning
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Berdakh Abibullaev1539.41
Jinung An211520.43