Article thumbnail

Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b

By Kai Keng Ang, Zheng Yang Chin, Chuanchu Wang, Cuntai Guan and Haihong Zhang


The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10 × 10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively

Topics: Neuroscience
Publisher: Frontiers Research Foundation
OAI identifier:
Provided by: PubMed Central

Suggested articles


  1. (2007). An algorithm for idle-state detection in motorimagery-based brain-computer interface.
  2. Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations.N e wY o r k :
  3. (2004). Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms.
  4. (2007). Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment.
  5. (2008). Classifying single-trial EEG during motor imagery by iterativespatio-spectralpatternslearning (ISSPL).
  6. (2006). Combined optimization of spatial and temporal filters for improving brain-computer interfacing.IEEETrans.Biomed.Eng.
  7. (1999). Designing optimal spatial filters for singletrial EEG classification in a movement task.
  8. (1979). Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement.
  9. (1999). Event-related EEG/MEG synchronizationanddesynchronization: basic principles.
  10. (2008). Filter bank common spatial pattern (FBCSP) in brain-computer interface,”
  11. (2012). Filter bank common spatial pattern algorithm on
  12. (1990). Introduction to Statistical Pattern Recognition, 2nd Edn. NewYork:
  13. Müller,K.-R.(2005).Spatio-spectral filters for improving the classification of single trial EEG.
  14. (2008). Multiclass common spatial patterns and information theoretic feature extraction.
  15. (2009). Multiclassfilterbankcommonspatialpattern for four-class motor imagery BCI,”
  16. (1962). On estimation of a probability density function and mode.
  17. (2000). Optimal spatial filtering of single trial EEG during imagined hand movement.
  18. (2008). Optimizing spatial filters for robust EEG single-trial analysis.
  19. (2005). RSPOP: rough set-based pseudo outer-product fuzzy rule identification algorithm.
  20. (2006). Spectrally Weighted Common Spatial Pattern Algorithm for Single Trial EEG Classification. Mathematical Engineering Technical Reports,
  21. (2012). Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
  22. (2000). Statistical pattern recognition: a review.
  23. (2012). Supervised pseudo self-evolving cerebellar algorithm for generating fuzzy membership functions.
  24. (2008). The Berlin brain– computer interface: accurate performance from first-session in BCInaive subjects.
  25. (2007). The non-invasive Berlin brain-computer interface: fast acquisition of effective performance in untrained subjects.
  26. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits noncommercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.