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On robust classification of hemodynamic signals for BCIs via multiple kernel Nu-SVM

Title
On robust classification of hemodynamic signals for BCIs via multiple kernel Nu-SVM
Authors
Abibullaev, BerdakhAn, Jin Ung
DGIST Authors
Abibullaev, Berdakh; An, Jin Ung
Issue Date
2016
Citation
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, 2016-November, 3063-3068
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
ISSN
2153-0858
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. © 2016 IEEE.
URI
http://hdl.handle.net/20.500.11750/1736
DOI
10.1109/IROS.2016.7759474
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
Files:
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Collection:
ETC2. Conference Papers
Division of IoT∙Robotics Convergence Research2. Conference Papers


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