Channel Reduction by Cultural-Based Multi-objective Particle Swarm Optimization Based on Filter Bank in Brain–Computer Interfaces

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 238)


Applying many electrodes is undesirable for real-life brain–computer interface (BCI) application since the recording preparation can be troublesome and time-consuming. This chapter presented a novel channel selection method, named cultural-based multi-objective particle swarm optimization (CMOPSO) based on filter bank, which introduced a cultural framework to adapt the personalized flight parameters of the mutated particles. A filter bank was designed using a coefficient decimation (CD) technology. The broad frequency band (8–30 Hz) is divided into ten subbands with width 4 Hz and overlapping 2 Hz, and the channel selection algorithm was applied to each subband. The optimal channels were chosen from the best channels derived from each subband. The algorithm was tested on five four-class data sets and the experimental results showed that the approach outperforms the broad band approach in selecting a smaller subset of channels without the sacrifice of classification accuracy.


Filter Bank Motor Imagery Channel Selection Common Spatial Pattern Fisher Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was funded by National Natural Science Foundation of China (# 60965004).


  1. 1.
    Guger C, Ramoser H, Pfurtscheller G (2000) Real-time EEG analysis with subject-specific spatial patterns for a brain computer interface (BCI). IEEE Trans Rehabil Eng 8:447–456CrossRefGoogle Scholar
  2. 2.
    Wolpaw JR, Birbaumer N, Heetderks W (2000) Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8(2):164–173CrossRefGoogle Scholar
  3. 3.
    Blankertz B, Tomioka R, Lemm S et al (2008) Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag 25(1):41–56CrossRefGoogle Scholar
  4. 4.
    Pfurtscheller G, da Silva FHL (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110:1842–1857CrossRefGoogle Scholar
  5. 5.
    Tichavsky P, Yeredor A (2009) Fast approximate joint diagonalization incorporating weight matrces. IEEE Trans Signal Process 57(3):878–891MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chen K, Wei Q, Ma Y (2010) An unweigted exhaustive diagonalization based multi-class common spatial pattern algorithm in brain-computer interfaces. In: Proceedings of the 2nd international conference on information engineering and computer science, vol 1, Wuhan, China, pp 206–210Google Scholar
  7. 7.
    Mahesh R, Vinod AP (2008) Coefficient decimation approach for realizing reconfigurable finite impulse response filters. In: Proceedings of IEEE ISCAS, pp 81–84Google Scholar
  8. 8.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of international joint conference on neural networks, Perth, Australia, pp 1942–1948Google Scholar
  9. 9.
    Fan LSS and Chang JM (2007) A modified particle swarm optimizer using an adaptive dynamic weigh scheme. In: Proceedings of international conference on digital human modeling, Beijing, China, pp 56–65Google Scholar
  10. 10.
    Jun L, Meichun L (2008) Common spatial pattern and particle swarm optimization for channel selection in BCI. In: Proceedings of the 3rd international conference on innovative computing information and control (ICICIC ’08), pp 457–457Google Scholar
  11. 11.
    Duda R, Hart P, Stork D (2001) Pattern classification, 2nd edn. Wiley, New YorkMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Electronic EngineeringNanchang UniversityNanchangChina

Personalised recommendations