Fuzzy entropy for Feature optimization In Motor Imagery based Brain Computer Interface

  • Vrushali Raut
  • Sanjay Ganorkar


In non-invasive Motor Imagery (MI) based
Brain Computer Interface, variation due to MI has spread not
only in time domain but also in frequency domain. Even
channels are also occupied by this spread. Thus number of
features belonging to all these variations is responsible for
classifying the underlying task. This paper works on feature
optimization using fuzzy entropy so as to avoid under as well
over fitting of classifier. Time-Frequency correlation of the
signal is obtained using wavelet transform. Second and third
order statistical features are extracted from wavelet bands.
SVM and KNN with kernel variations are used for
classification. Outcome of this experimenting leads to accuracy
of 93.7% for optimized features using fuzzy entropy compared
to less than 90% for features without optimization.

Keywords: Motor Imagery (MI), Brain Computer Interface (BCI), Fuzzy Entropy


[1] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T.
M. Vaughan, “Brain–computer interfaces for communication and
control,” Clin. Neurophysiol., vol. 113, no. 6, pp. 767–791, Jun. 2002.
[2] Y. Zhang, G. Zhou, J. Jin, X. Wang, and A. Cichocki, “SSVEP
recognition using common feature analysis in brain-computer
interface,” J. Neurosci. Methods, vol. 244, pp. 8–15, 2015.
[3] G. Prasad, P. Herman, D. Coyle, S. McDonough, and J. Crosbie,
“Applying a brain-computer interface to support motor imagery
practice in people with stroke for upper limb recovery: A feasibility
study,” J. Neuroeng. Rehabil., vol. 7, no. 1, pp. 1–17, 2010.
[4] S. G. Mason and G. E. Birch, “A general framework for brain -
Computer interface design,” IEEE Trans. Neural Syst. Rehabil. Eng.,
vol. 11, no. 1, pp. 70–85, 2003.
[5] P. Speller and M. J. Alhaddad, “Common Average Reference ( CAR )
Improves Common Average Reference ( CAR ) Improves P300
Speller,” Int. J. Eng. Technol. Vol., vol. 2, no. March, 2012.
[6] T. Wang and B. He, “An efficient rhythmic component expression and
weighting synthesis strategy for classifying motor imagery EEG in a
brain-computer interface,” J. Neural Eng., vol. 1, no. 1, pp. 1–7,
[7] C. E. Tenke and J. Kayser, “Surface Laplacians ( SL ) and phase
properties of EEG rhythms : Simulated generators in a volumeconduction
model,” Int. J. Psychophysiol., vol. 97, no. 3, pp. 285–
298, 2015.
[8] B. Xu and A. Song, “Pattern recognition of motor imagery EEG using
wavelet transform,” J. Biomed. Sci. Eng., vol. 1, no. May, p. 64,
[9] M. E. Elhalawani and A. S. Tolba, “Brain – Computer Interaction
( BCI ) Using a Fast Algorithm for Empirical Mode Decomposition
( EMD ) and a Specialized Hardware,” Int. J. Inf. Sci. Intell. Syst.,
vol. 3, no. 1, pp. 169–178, 2014.
[10] S. Pittner and S. V. Kamarthi, “Feature extraction from wavelet
coefficients for pattern recognition tasks,” IEEE Trans. Pattern Anal.
Mach. Intell., vol. 21, no. 1, pp. 83–88, 1999.
[11] P. Luukka, “Expert Systems with Applications Feature selection using
fuzzy entropy measures with similarity classifier,” Expert Syst. Appl.,
vol. 38, no. 4, pp. 4600–4607, 2011.
[12] P. Herman, G. Prasad, S. Member, T. M. Mcginnity, and D. Coyle,
“Comparative Analysis of Spectral Approaches to Feature Extraction
for EEG-Based Motor Imagery Classification,” vol. 16, no. 4, pp.
317–327, 2008.
[13] R. Chai et al., “Brain – Computer Interface Classifier for Wheelchair
Commands Using Neural Network With Fuzzy Particle Swarm
Optimization,” IEEE J. Biomed. Heal. INFORMATICS, vol. 18, no.
5, pp. 1614–1624, 2014.
[14] Y. Kutlu and D. Kuntalp, “Feature extraction for ECG heartbeats
using higher order statistics of WPD coefficients,” Comput. Methods
Programs Biomed., vol. 105, no. 3, pp. 257–267, 2011.
[15] D. Li, W. Pedrycz, and N. J. Pizzi, “Fuzzy wavelet packet based
feature extraction method and its application to biomedical signal
classification,” IEEE Trans. Biomed. Eng., vol. 52, no. 6, pp. 1132–
1139, 2005.
[16] B. Blankertz et al., “Classifying Single Trial EEG : Towards Brain
Computer Interfacing,” Adv. Neural Inf. Proc. Syst. 14 (NIPS 01), no.
c, pp. 96–98, 2002.
[17] K. V. Bulusu and M. W. Plesniak, “Shannon entropy-based wavelet
transform method for autonomous coherent structure identification in
fluid flow field data,” Entropy, vol. 17, no. 10, pp. 6617–6642, 2015.
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How to Cite
Raut, V., & Ganorkar, S. (2019). Fuzzy entropy for Feature optimization In Motor Imagery based Brain Computer Interface. Asian Journal For Convergence In Technology (AJCT). Retrieved from http://asianssr.org/index.php/ajct/article/view/786