Classification of mental tasks using stockwell transform

In recent years, various physiological signal based rehabilitation systems have been developed for the physically disabled in which electroencephalographic (EEG) signal is one among them. The efficiency of such a system depends upon the signal processing and classification algorithms. In order to develop an EEG based rehabilitation or assistive system, it is necessary to develop an effective EEG signal processing algorithm. This paper proposes Stockwell transform (ST) based analysis of EEG dynamics during different mental tasks. EEG signals from Keirn and Aunon database were used in this study. Three classifiers were employed such as k-means nearest neighborhood (kNN), linear discriminant analysis (LDA) and support vector machine (SVM) to test the strength of the proposed features. Ten-fold cross validation method was used to demonstrate the consistency of the classification results. Using the proposed method, an average accuracy ranging between 84.72% and 98.95% was achieved for multi-class problems (five mental tasks).

[1]  M.Fedias,et al.  Linear Discriminant Analysis LDA and logic fusion of Color decisions to face authentication , 2014 .

[2]  Sazali Yaacob,et al.  Classification of Speech Dysfluencies Using LPC Based Parameterization Techniques , 2012, Journal of Medical Systems.

[3]  Narasimhan Sundararajan,et al.  Classification of Mental Tasks from Eeg Signals Using Extreme Learning Machine , 2006, Int. J. Neural Syst..

[4]  Muhsin Tunay Gençoglu,et al.  An expert system based on S-transform and neural network for automatic classification of power quality disturbances , 2009, Expert Syst. Appl..

[5]  Cheng-Han Lee,et al.  Classification of electroencephalography (EEG) signals for different mental activities using Kullback Leibler (KL) divergence , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  M. Hariharan,et al.  MFCC based recognition of repetitions and prolongations in stuttered speech using k-NN and LDA , 2009, 2009 IEEE Student Conference on Research and Development (SCOReD).

[7]  Ping Wang,et al.  Improving Mental Task Classification by Adding High Frequency Band Information , 2008, Journal of Medical Systems.

[8]  M. Hariharan,et al.  Comparison of performance using Daubechies Wavelet family for facial expression recognition , 2010, 2010 6th International Colloquium on Signal Processing & its Applications.

[9]  R. Palaniappan,et al.  Utilizing Gamma Band to Improve Mental Task Based Brain-Computer Interface Design , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  M. Hariharan,et al.  Mental tasks classifications using S-transform for BCI applications , 2011, 2011 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT).

[11]  Li Zhiwei,et al.  Classification of Mental Task EEG Signals Using Wavelet Packet Entropy and SVM , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[12]  Robert Glenn Stockwell,et al.  A basis for efficient representation of the S-transform , 2007, Digit. Signal Process..

[13]  Kok-Kiong Poh,et al.  Analysis of Neonatal EEG Signals using Stockwell Transform , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  D. Yao,et al.  An Enhanced Probabilistic LDA for Multi-Class Brain Computer Interface , 2011, PloS one.

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

[16]  J. Montaño,et al.  Wavelet and neural structure: a new tool for diagnostic of power system disturbances , 2001 .

[17]  S. Rezaei,et al.  Comparison of Five Different Classifiers for Classification of Mental Tasks , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.