Fuzzy clustering-based feature extraction method for mental task classification

A brain computer interface (BCI) is a communication system by which a person can send messages or requests for basic necessities without using peripheral nerves and muscles. Response to mental task-based BCI is one of the privileged areas of investigation. Electroencephalography (EEG) signals are used to represent the brain activities in the BCI domain. For any mental task classification model, the performance of the learning model depends on the extraction of features from EEG signal. In literature, wavelet transform and empirical mode decomposition are two popular feature extraction methods used to analyze a signal having non-linear and non-stationary property. By adopting the virtue of both techniques, a theoretical adaptive filter-based method to decompose non-linear and non-stationary signal has been proposed known as empirical wavelet transform (EWT) in recent past. EWT does not work well for the signals having overlapped in frequency and time domain and failed to provide good features for further classification. In this work, Fuzzy c-means algorithm is utilized along with EWT to handle this problem. It has been observed from the experimental results that EWT along with fuzzy clustering outperforms in comparison to EWT for the EEG-based response to mental task problem. Further, in case of mental task classification, the ratio of samples to features is very small. To handle the problem of small ratio of samples to features, in this paper, we have also utilized three well-known multivariate feature selection methods viz. Bhattacharyya distance (BD), ratio of scatter matrices (SR), and linear regression (LR). The results of experiment demonstrate that the performance of mental task classification has improved considerably by aforesaid methods. Ranking method and Friedman’s statistical test are also performed to rank and compare different combinations of feature extraction methods and feature selection methods which endorse the efficacy of the proposed approach.

[1]  F Babiloni,et al.  Linear classification of low-resolution EEG patterns produced by imagined hand movements. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[2]  R. K. Agrawal,et al.  Performance enhancement of mental task classification using EEG signal: a study of multivariate feature selection methods , 2015, Soft Comput..

[3]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[4]  Brendan Z. Allison,et al.  Brain-Computer Interfaces , 2010 .

[5]  Hans Berger,et al.  Über das Elektrenkephalogramm des Menschen , 1932, Archiv für Psychiatrie und Nervenkrankheiten.

[6]  Brendan Z. Allison,et al.  Brain-Computer Interfaces: A Gentle Introduction , 2009 .

[7]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[8]  M. Friedman.  The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[9]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[10]  Helge J. Ritter,et al.  BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm , 2004, IEEE Transactions on Biomedical Engineering.

[11]  Abraham Lempel,et al.  On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.

[12]  Yaacob Sazali,et al.  Classification of human emotion from EEG using discrete wavelet transform , 2010 .

[13]  Pedro J. García-Laencina,et al.  Efficient Automatic Selection and Combination of EEG Features in Least Squares Classifiers for Motor Imagery Brain-Computer Interfaces , 2013, Int. J. Neural Syst..

[14]  M J Stokes,et al.  EEG-based communication: a pattern recognition approach. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[15]  Michel Verleysen,et al.  EEG feature selection using mutual information and support vector machine: A comparative analysis , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[16]  Richard Bellman,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[17]  Gerwin Schalk,et al.  Brain–computer symbiosis , 2008, Journal of neural engineering.

[18]  Hung T. Nguyen,et al.  A note on the extension principle for fuzzy sets , 1978 .

[19]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[20]  Amit Konar,et al.  Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata , 2013, Medical & Biological Engineering & Computing.

[21]  Tae-Seong Kim,et al.  A P300-based brain computer interface system for words typing , 2014, Comput. Biol. Medicine.

[22]  G. Pfurtscheller,et al.  EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.

[23]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  H. Berger.  Über das Elektrenkephalogramm des Menschen , 1938, Archiv für Psychiatrie und Nervenkrankheiten.

[26]  Sung-Bae Cho,et al.  Forward selection method with regression analysis for optimal gene selection in cancer classification , 2007, Int. J. Comput. Math..

[27]  José Higino Correia,et al.  Feature Selection for Brain-Computer Interface , 2009 .

[28]  Y. Istefanopulos,et al.  IEEE Engineering in Medicine and Biology Society , 2019, IEEE Transactions on Biomedical Engineering.

[29]  M.M. Moore,et al.  Real-world applications for brain-computer interface technology , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[30]  Naveen Kumar,et al.  Improved fuzzy entropy clustering algorithm for MRI brain image segmentation , 2014, Int. J. Imaging Syst. Technol..

[31]  H. Chernoff.  A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations , 1952 .

[32]  Z. Keirn,et al.  A new mode of communication between man and his surroundings , 1990, IEEE Transactions on Biomedical Engineering.

[33]  Ah Chung Tsoi,et al.  Classification of EEG signals using the wavelet transform , 1997, Proceedings of 13th International Conference on Digital Signal Processing.

[34]  Pablo F. Diez,et al.  Application of the empirical mode decomposition to the extraction of features from EEG signals for mental task classification , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[35]  S. Nishida,et al.  A new brain-computer interface design using fuzzy ARTMAP , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[36]  Rabab K Ward,et al.  A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals , 2007, Journal of neural engineering.

[37]  R. K. Agrawal,et al.  A three phase approach for mental task classification using EEG , 2012, ICACCI '12.

[38]  Michael Tangermann,et al.  Feature selection for brain-computer interfaces , 2007 .

[39]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[40]  R. K. Agrawal,et al.  Relevant Feature Selection from EEG Signal for Mental Task Classification , 2012, PAKDD.

[41]  Steven J. Schiff,et al.  Feature selection on movement imagery discrimination and attention detection , 2010, Medical & Biological Engineering & Computing.

[42]  C.W. Anderson,et al.  Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks , 1998, IEEE Transactions on Biomedical Engineering.

[43]  Samy Bengio,et al.  HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems , 2004, ESANN.

[44]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[45]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[46]  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.

[47]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .