Extreme energy difference for feature extraction of EEG signals

[1]  Elif Derya íbeyli Measuring saliency of features representing EEG signals using signal-to-noise ratios , 2009 .

[2]  Elif Derya Übeyli Measuring saliency of features representing EEG signals using signal-to-noise ratios , 2009, Expert Syst. Appl..

[3]  Shiliang Sun,et al.  The Extreme Energy Ratio Criterion for EEG Feature Extraction , 2008, ICANN.

[4]  Shiliang Sun,et al.  The random electrode selection ensemble for EEG signal classification , 2008, Pattern Recognit..

[5]  Shiliang Sun,et al.  An experimental evaluation of ensemble methods for EEG signal classification , 2007, Pattern Recognit. Lett..

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

[7]  José del R. Millán,et al.  Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Changshui Zhang,et al.  Adaptive feature extraction for EEG signal classification , 2006, Medical and Biological Engineering and Computing.

[9]  Shiliang Sun,et al.  An optimal kernel feature extractor and its application to EEG signal classification , 2006, Neurocomputing.

[10]  Elif Derya Übeyli,et al.  Recurrent neural networks employing Lyapunov exponents for EEG signals classification , 2005, Expert Syst. Appl..

[11]  B. Kamousi,et al.  Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[13]  Fusheng Yang,et al.  BCI competition 2003-data set IV:An algorithm based on CSSD and FDA for classifying single-trial EEG , 2004, IEEE Transactions on Biomedical Engineering.

[14]  Michael J. Black,et al.  Modeling and decoding motor cortical activity using a switching Kalman filter , 2004, IEEE Transactions on Biomedical Engineering.

[15]  Klaus-Robert Müller,et al.  Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms , 2004, IEEE Transactions on Biomedical Engineering.

[16]  William Z Rymer,et al.  Guest Editorial Brain–Computer Interface Technology: A Review of the Second International Meeting , 2001 .

[17]  K.-R. Muller,et al.  Linear and nonlinear methods for brain-computer interfaces , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  P. Sajda,et al.  A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  E. Curran,et al.  Learning to control brain activity: A review of the production and control of EEG components for driving brain–computer interface (BCI) systems , 2003, Brain and Cognition.

[20]  Michael J. Black,et al.  Connecting brains with machines: the neural control of 2D cursor movement , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[21]  Touradj Ebrahimi,et al.  Brain-computer interface in multimedia communication , 2003, IEEE Signal Process. Mag..

[22]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[23]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[24]  Miguel A. L. Nicolelis,et al.  Actions from thoughts , 2001, Nature.

[25]  Klaus-Robert Müller,et al.  Classifying Single Trial EEG: Towards Brain Computer Interfacing , 2001, NIPS.

[26]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[27]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[28]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[29]  G. Pfurtscheller,et al.  Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.

[30]  Patrick Berg,et al.  Common spatial subspace decomposition applied to analysis of brain responses under multiple task conditions: a simulation study , 1999, Clinical Neurophysiology.

[31]  P. Nunez,et al.  A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging. , 1994, Electroencephalography and clinical neurophysiology.

[32]  H. Lüders,et al.  American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature , 1991, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[33]  S. R. Searle Matrix Algebra Useful for Statistics , 1983 .