Common Spatial Patterns Based on the Quantized Minimum Error Entropy Criterion

Badong Chen, Yuanhao Li, Jiyao Dong, Na Lu, Jing Qin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

23 Citations (Scopus)


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 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.

Original languageEnglish
Article number8423432
Pages (from-to)4557-4568
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number11
Publication statusPublished - Nov 2020


  • Brain-computer interfaces (BCIs)
  • common spatial patterns (CSPs)
  • electroencephalogram (EEG)
  • quantized minimum error entropy (QMEE)
  • robustness

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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