An efficient hybrid linear and kernel CSP approach for EEG feature extraction
作者： Behrooz Nasihatkon , Reza Boostani , Mansoor Zolghadri Jahromi
DOI: 10.1016/J.NEUCOM.2009.07.012
关键词: Artificial intelligence 、 Kernel embedding of distributions 、 Generalization 、 Kernel method 、 Nonlinear system 、 Context (language use) 、 Kernel (statistics) 、 Radial basis function kernel 、 Mathematics 、 Pattern recognition 、 Space (mathematics)
摘要: Common spatial patterns (CSP) has proved to be very successful in EEG feature extraction. To relax the presumption of strictly linear CSP, nonlinear variants approach are proposed using kernel method. However, they typically suffer from two main drawbacks: problem complexity and low generalization ability dealing with different subjects. overcome these drawbacks, this paper, effective solutions proposed. First, data bunching low-dimensional space is used solve problem. The second tackled by choosing appropriate functions, which take into account small amounts nonlinearity a generally context brain patterns, also able adapted fit each certain case.
elsevier.com
sci-hub.se 参考文章(12)
M. A. Aizerman, Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning Automation and Remote Control. ,vol. 25, pp. 821- 837 ,(1964)
Bernhard Schölkopf, Alexander J. Smola, Learning with Kernels The MIT Press. pp. 626- ,(2018) , 10.7551/MITPRESS/4175.001.0001
Jiacai Zhang, Jianjun Tang, Li Yao, Optimizing spatial filters with kernel methods for BCI applications MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications. ,vol. 6790, ,(2007) , 10.1117/12.743539
Z.J. Koles, The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG Electroencephalography and Clinical Neurophysiology. ,vol. 79, pp. 440- 447 ,(1991) , 10.1016/0013-4694(91)90163-X
Shiliang Sun, Changshui Zhang, Letters: An optimal kernel feature extractor and its application to EEG signal classification Neurocomputing. ,vol. 69, pp. 1743- 1748 ,(2006) , 10.1016/J.NEUCOM.2005.12.125
Carlos M. Jarque, Anil K. Bera, Efficient tests for normality, homoscedasticity and serial independence of regression residuals Economics Letters. ,vol. 6, pp. 255- 259 ,(1980) , 10.1016/0165-1765(80)90024-5
Johannes Müller-Gerking, Gert Pfurtscheller, Henrik Flyvbjerg, Designing optimal spatial filters for single-trial EEG classification in a movement task Clinical Neurophysiology. ,vol. 110, pp. 787- 798 ,(1999) , 10.1016/S1388-2457(98)00038-8
K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, B. Scholkopf, An introduction to kernel-based learning algorithms IEEE Transactions on Neural Networks. ,vol. 12, pp. 181- 201 ,(2001) , 10.1109/72.914517
B. Obermaier, C. Neuper, C. Guger, G. Pfurtscheller, Information transfer rate in a five-classes brain-computer interface international conference of the ieee engineering in medicine and biology society. ,vol. 9, pp. 283- 288 ,(2001) , 10.1109/7333.948456
H. Ramoser, J. Muller-Gerking, G. Pfurtscheller, Optimal spatial filtering of single trial EEG during imagined hand movement international conference of the ieee engineering in medicine and biology society. ,vol. 8, pp. 441- 446 ,(2000) , 10.1109/86.895946