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Common Spatial Patterns Based on the Quantized Minimum Error Entropy Criterion.

Authors :
Chen, Badong
Li, Yuanhao
Dong, Jiyao
Lu, Na
Qin, Jing
Source :
IEEE Transactions on Systems, Man & Cybernetics. Systems; Nov2020, Vol. 50 Issue 11, p4557-4568, 12p
Publication Year :
2020

Abstract

Common spatial pattern (CSP) is a classic method commonly used in multichannel electroencephalogram (EEG) signal processing, which aims to extract effective features for binary classification by solving spatial filters that maximize the ratio of filtered dispersion between two classes. The aim of this paper is to improve the performance of the conventional CSP method, which will be badly influenced by noises. The recently proposed quantized minimum error entropy (QMEE) criterion is applied to structure a new objective function instead of the ${L} _{2}$ -norm in the conventional CSP. Quantization is utilized to reduce the computational complexity. The new objective function is optimized by a gradient-based iterative algorithm. The desirable performance of the QMEE-based CSP method, namely CSP-QMEE, is demonstrated with a toy example and two real EEG datasets, including Dataset IIb of the brain–computer interfaces (BCIs) Competition IV (three channels) and Dataset IIIa of the BCI Competition III (60 channels). The new method can achieve satisfactory performance compared to existing methods on all datasets. The promising results in this paper suggest that the CSP-QMEE may become a powerful tool for BCIs. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21682216
Volume :
50
Issue :
11
Database :
Complementary Index
Journal :
IEEE Transactions on Systems, Man & Cybernetics. Systems
Publication Type :
Academic Journal
Accession number :
146472527
Full Text :
https://doi.org/10.1109/TSMC.2018.2855106