Investigation of the most appropriate mother wavelet for characterizing imaginary EEG signals used in BCI systems

Feature extraction is a very challenging task, since choosing discriminative features directly affects the recognition rate of the brain–computer interface (BCI) system. The objective of this paper is to investigate the effect of mother wavelets (MWs) on classification results. To this end, features were extracted from 3 different datasets using 12 MWs, and then the signals were classified using 3 classification algorithms, including k-nearest neighbor, support vector machine, and linear discriminant analysis. The experiments proved that Daubechies and Shannon were the most suitable wavelet families for extracting more discriminative features from imaginary EEG/ECoG signals.

[1]  T.M. McGinnity,et al.  Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Yuan-chin Ivan Chang,et al.  Meta-learning for imbalanced data and classification ensemble in binary classification , 2009, Neurocomputing.

[3]  Onder Aydemir,et al.  Wavelet Transform Based Classification of Invasive Brain Computer Interface Data , 2011 .

[4]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[5]  Berj L. Bardakjian,et al.  A Wavelet Packet-Based Algorithm for the Extraction of Neural Rhythms , 2009, Annals of Biomedical Engineering.

[6]  Klaus-Robert Müller,et al.  The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials , 2004, IEEE Transactions on Biomedical Engineering.

[7]  A. Al-Ani,et al.  Brain-Computer Interface Analysis using Continuous Wavelet Transform and Adaptive Neuro-Fuzzy Classifier , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Juan Ignacio Godino-Llorente,et al.  Feature Extraction From Parametric Time–Frequency Representations for Heart Murmur Detection , 2010, Annals of Biomedical Engineering.

[9]  P. de Chazal,et al.  A parametric feature extraction and classification strategy for brain-computer interfacing , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Vladimir Bostanov,et al.  BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram , 2004, IEEE Transactions on Biomedical Engineering.

[11]  Ali Khazaee,et al.  High Efficient System for Automatic Classification of the Electrocardiogram Beats , 2011, Annals of Biomedical Engineering.

[12]  Bernhard Schölkopf,et al.  Methods Towards Invasive Human Brain Computer Interfaces , 2004, NIPS.

[13]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .

[14]  Shiliang Sun,et al.  Semi-supervised feature extraction for EEG classification , 2012, Pattern Analysis and Applications.

[15]  H. Flor,et al.  The thought translation device (TTD) for completely paralyzed patients. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[16]  V. Samar,et al.  Wavelet Analysis of Neuroelectric Waveforms: A Conceptual Tutorial , 1999, Brain and Language.

[17]  Wei-Yen Hsu,et al.  EEG-based motor imagery analysis using weighted wavelet transform features , 2009, Journal of Neuroscience Methods.

[18]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[19]  K.-R. Muller,et al.  Linear and nonlinear methods for brain-computer interfaces , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[21]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[22]  M. Kamrunnahar,et al.  Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces , 2011, Annals of Biomedical Engineering.

[23]  G. Pfurtscheller,et al.  Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.