Neural Network Approaches for EEG Classification

This chapter is aimed at providing a state-of-the-art review of the prominent neural network based approaches that can be employed for EEG classification. The chapter consists of five major sections. Following a short introduction, the next two sections are devoted to the discussions about different feature extraction algorithms and ANN based classifiers employed for EEG signals. Several representative schemes are mentioned in a nutshell, which show how diverse schemes can be employed, with good effect, to solve a similar type of problem. Here it should be kept in mind that this in no way indicates that the works mentioned in the reference within a genre either reflect the most suitable ones available within this sub-category or present the exhaustive list of references. For example we have put several references of the schemes employing Discrete Wavelet Transform (DWT) based feature extraction scheme, in the context of EEG classifiers. But this does neither indicate that they present a complete list of works utilizing DWT nor do they indicate that similar types of other works carried out, utilizing DWT, are less useful. We apologize for the fact that we may have not been able to accommodate several such suitable algorithms, within one or more sub-categories, within the boundary of this discussion.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  I. Guler,et al.  Multiclass Support Vector Machines for EEG-Signals Classification , 2007, IEEE Transactions on Information Technology in Biomedicine.

[3]  Toshio Tsuji,et al.  Pattern classification of EEG signals using a log-linearized Gaussian mixture neural network , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[4]  V.A. Maiorescu,et al.  Classification of EEG signals represented by AR models for cognitive tasks - a neural network based method , 2003, Signals, Circuits and Systems, 2003. SCS 2003. International Symposium on.

[5]  Yasar Becerikli,et al.  On three intelligent systems: dynamic neural, fuzzy, and wavelet networks for training trajectory , 2004, Neural Computing & Applications.

[6]  Khashayar Khorasani,et al.  An Adaptive Structure Neural Networks with Application to EEG Automatic Seizure Detection , 1996, Neural Networks.

[7]  O. Ozdamar,et al.  Wavelet preprocessing for automated neural network detection of EEG spikes , 1995 .

[8]  Bao-Liang Lu,et al.  Massively parallel classification of single-trial EEG signals using a min-max Modular neural network , 2004, IEEE Transactions on Biomedical Engineering.

[9]  Guang Li,et al.  EEG Recognition Based on Multiple Types of Information by Using Wavelet Packet Transform and Neural Networks , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[10]  V. Srinivasan,et al.  Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks , 2007, IEEE Transactions on Information Technology in Biomedicine.

[11]  Luis Diambra,et al.  Epileptic activity recognition in EEG recording , 1999 .

[12]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Abdulhamit Subasi.  Automatic detection of epileptic seizure using dynamic fuzzy neural networks , 2006, Expert Syst. Appl..

[14]  Steven M. Pincus,et al.  A regularity statistic for medical data analysis , 1991, Journal of Clinical Monitoring.

[15]  J. Bruhn,et al.  Approximate Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect during Desflurane Anesthesia , 2000, Anesthesiology.

[16]  Abdulhamit Subasi,et al.  Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction , 2007, Comput. Biol. Medicine.

[17]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[18]  Chang-Beom Ahn,et al.  EEG and artifact classification using a neural network , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Yasar Becerikli,et al.  Trajectory priming with dynamic fuzzy networks in nonlinear optimal control , 2004, IEEE Transactions on Neural Networks.

[21]  Kenneth Revett,et al.  EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks , 2006, IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA'06).

[22]  Toshio Tsuji,et al.  Pattern classification of time-series EEG signals using neural networks , 1996, Proceedings 5th IEEE International Workshop on Robot and Human Communication. RO-MAN'96 TSUKUBA.

[23]  Kumar,et al.  Neural Networks a Classroom Approach , 2004 .

[24]  G Pfurtscheller,et al.  Using time-dependent neural networks for EEG classification. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[25]  Abdulhamit Subasi,et al.  EEG signal classification using wavelet feature extraction and a mixture of expert model , 2007, Expert Syst. Appl..

[26]  J Gotman,et al.  State-dependent spike detection: concepts and preliminary results. , 1991, Electroencephalography and clinical neurophysiology.

[27]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[28]  N. K. Bose,et al.  Neural Network Fundamentals with Graphs, Algorithms and Applications , 1995 .

[29]  N Pradhan,et al.  Detection of seizure activity in EEG by an artificial neural network: a preliminary study. , 1996, Computers and biomedical research, an international journal.

[30]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[31]  Nurettin Acir,et al.  Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks , 2005, IEEE Transactions on Biomedical Engineering.