Classification of EEG signals using feature creation produced by grammatical evolution

A state-of-the-art method based on a grammatical evolution approach is utilized in this study to classify EEG signals. The method is able to construct nonlinear mappings of the original features in order to improve their effectiveness when used as input into artificial intelligence techniques. Several features are initially extracted from the EEG signals which are subsequently used to create the non-linear mappings. Then, a classification stage is applied, using multi-layer perceptron (MLP) and radial basis functions (RBF), to categorize the EEG signals. The proposed method is evaluated using a benchmark epileptic EEG dataset and promising results are reported.

[1]  George K. Georgoulas,et al.  Grammatical evolution for features of epileptic oscillations in clinical intracranial electroencephalograms , 2011, Expert Syst. Appl..

[2]  Elif Derya íbeyli.  Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines , 2008 .

[3]  Dimitrios I. Fotiadis,et al.  EEG Transient Event Detection and Classification Using Association Rules , 2006, IEEE Transactions on Information Technology in Biomedicine.

[4]  A. Tzallas,et al.  Automated Epileptic Seizure Detection Methods: A Review Study , 2012 .

[5]  Amitava Chatterjee,et al.  Cross-correlation aided support vector machine classifier for classification of EEG signals , 2009, Expert Syst. Appl..

[6]  Dimitrios I. Fotiadis,et al.  A Review of Automated Methodologies for the Detection of Epileptic Episodes Using Long-Term EEG Signals , 2016 .

[7]  U. Rajendra Acharya,et al.  Entropies for detection of epilepsy in EEG , 2005, Comput. Methods Programs Biomed..

[8]  Chrysostomos D. Stylios,et al.  Novel approach for fetal heart rate classification introducing grammatical evolution , 2007, Biomed. Signal Process. Control..

[9]  Conor Ryan,et al.  Grammatical Evolution , 2001, Genetic Programming Series.

[10]  Leonidas D. Iasemidis,et al.  ■ REVIEW : Chaos Theory and Epilepsy , 1996 .

[11]  Aruna Tiwari,et al.  A novel genetic programming approach for epileptic seizure detection , 2016, Comput. Methods Programs Biomed..

[12]  Ilker Yaylali,et al.  Interictal spike detection using the Walsh transform , 2004, IEEE Transactions on Biomedical Engineering.

[13]  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.

[14]  Dimitrios I. Fotiadis,et al.  Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis , 2009, IEEE Transactions on Information Technology in Biomedicine.

[15]  Ioannis G. Tsoulos,et al.  Selecting and constructing features using grammatical evolution , 2008, Pattern Recognit. Lett..

[16]  Elif Derya Übeyli,et al.  Multiclass Support Vector Machines for EEG-Signals Classification , 2007, IEEE Transactions on Information Technology in Biomedicine.

[17]  Yi Chai,et al.  Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM , 2014, Biomed. Signal Process. Control..

[18]  Xin Liu,et al.  PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction , 2011, Comput. Intell. Neurosci..

[19]  A T Tzallas,et al.  A Method for Classification of Transient Events in EEG Recordings: Application to Epilepsy Diagnosis , 2006, Methods of Information in Medicine.

[20]  Dimitrios I. Fotiadis,et al.  Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks , 2007, Comput. Intell. Neurosci..

[21]  Abdulhamit Subasi,et al.  EEG signal classification using PCA, ICA, LDA and support vector machines , 2010, Expert Syst. Appl..

[22]  Leonidas D. Iasemidis,et al.  Chaos Theory and Epilepsy , 1996 .

[23]  Elif Derya Übeyli.  Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines , 2008, Comput. Biol. Medicine.

[24]  M Congedo,et al.  Classification of movement intention by spatially filtered electromagnetic inverse solutions , 2006, Physics in medicine and biology.

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

[26]  A. Cichocki,et al.  Diagnosis of Alzheimer's disease from EEG signals: where are we standing? , 2010 .

[27]  Derya íbeyliElif.  Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines , 2008 .

[28]  G Pfurtscheller,et al.  Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[29]  Bin He,et al.  Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns , 2004, Clinical Neurophysiology.

[30]  A. Cichocki,et al.  Diagnosis of Alzheimer's disease from EEG signals: where are we standing? , 2010, Current Alzheimer research.

[31]  Elif Derya Übeyli,et al.  Features extracted by eigenvector methods for detecting variability of EEG signals , 2007, Pattern Recognit. Lett..

[32]  Ioannis G. Tsoulos,et al.  Neural Recognition and Genetic Features Selection for Robust Detection of E-Mail Spam , 2006, SETN.