Biomedical Signal Processing and Control

Abstract In this study, three popular signal processing techniques (Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the decomposition of Electroencephalography (EEG) Signals in Brain Computer Interface (BCI) system for a classification task. Publicly available BCI competition III dataset IVa, a multichannel 2-class motor-imagery dataset, was used for this purpose. Multiscale Principal Component Analysis method was applied for the purpose of noise removal. In addition, different sets of features were formed to examine the effect of a particular group of features. The parameter selection process for signal decomposition methods was thoroughly explained as well. Our results show that the combination of Multiscale Principal Component Analysis de-noising and higher order statistics features extracted from wavelet packet decomposition sub-bands resulted in highest average classification accuracy of 92.8%. Our study is one among very few that provides a comprehensive comparison between signal decomposition methods in combination with higher order statistics in classification of BCI signals. In addition, we stressed the importance of higher frequency ranges in improving the classification task for EEG signals in Brain Computer Interface Systems. Obtained results indicate that the proposed model has the potential to obtain a reliable classification of motor imagery EEG signals, and can thus be used as a practical system for controlling a wheelchair. It can also further enhance the current rehabilitation therapies where appropriate feedback is delivered once the individual executes the correct movement. In that way, motor rehabilitation outcomes may improve over time.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  Jianjun Meng,et al.  Automated selecting subset of channels based on CSP in motor imagery brain-computer interface system , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[3]  Marcin Kolodziej,et al.  BRAIN-COMPUTER INTERFACE AS MEASUREMENT AND CONTROL SYSTEM THE REVIEW PAPER , 2012 .

[4]  Chang-Hwan Im,et al.  EEG-Based Brain-Computer Interfaces: A Thorough Literature Survey , 2013, Int. J. Hum. Comput. Interact..

[5]  U. Rajendra Acharya,et al.  Application of Higher Order Spectra to Identify Epileptic EEG , 2011, Journal of Medical Systems.

[6]  Mingai Li,et al.  The recognition of EEG with CSSD and SVM , 2012, Proceedings of the 10th World Congress on Intelligent Control and Automation.

[7]  Gerwin Schalk,et al.  Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000. , 2009, Journal of visualized experiments : JoVE.

[8]  Jasmin Kevric,et al.  The Impact of Mspca Signal De-Noising In Real-Time Wireless Brain Computer Interface System , 2015, SOCO 2015.

[9]  Wei Wu,et al.  Classifying Single-Trial EEG During Motor Imagery by Iterative Spatio-Spectral Patterns Learning (ISSPL) , 2008, IEEE Transactions on Biomedical Engineering.

[10]  D.J. McFarland,et al.  An Evaluation of Autoregressive Spectral Estimation Model Order for Brain-Computer Interface Applications , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Stefano Di Gennaro,et al.  Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis , 2015, Front. Comput. Neurosci..

[12]  Michael Unser,et al.  A review of wavelets in biomedical applications , 1996, Proc. IEEE.

[13]  Jasmin Kevric,et al.  The Effect of Multiscale PCA De-noising in Epileptic Seizure Detection , 2014, Journal of Medical Systems.

[14]  G. Pfurtscheller,et al.  The BCI competition III: validating alternative approaches to actual BCI problems , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  I. Jolliffe.  Principal Component Analysis , 2002 .

[16]  B. Bakshi.  Multiscale PCA with application to multivariate statistical process monitoring , 1998 .

[17]  Stefano Di Gennaro,et al.  CLASSIFICATION OF EEG SIGNALS FOR DETECTION OF EPILEPTIC SEIZURES BASED ON WAVELETS AND STATISTICAL PATTERN RECOGNITION , 2014 .

[18]  Haiping Lu,et al.  Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting , 2010, IEEE Transactions on Biomedical Engineering.

[19]  Yan Li,et al.  Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface , 2014, Comput. Methods Programs Biomed..

[20]  Pablo Laguna,et al.  Bioelectrical Signal Processing in Cardiac and Neurological Applications , 2005 .

[21]  Parham Ghorbanian,et al.  Discrete wavelet transform EEG features of Alzheimer'S disease in activated states , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  Abdulhamit Subasi,et al.  Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders , 2014, Journal of Medical Systems.

[23]  Yakup Kutlu,et al.  Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients , 2012, Comput. Methods Programs Biomed..

[24]  Ian H. Witten,et al.  WEKA: a machine learning workbench , 1994, Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference.

[25]  Abdulhamit Subasi,et al.  Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases , 2015, Circuits Syst. Signal Process..

[26]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[27]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[28]  Pedro J. García-Laencina,et al.  Automatic and Adaptive Classification of Electroencephalographic Signals for Brain Computer Interfaces , 2012, Journal of Medical Systems.