Motor Imagery Electroencephalogram Analysis Using Adaptive Neural-Fuzzy Classification

In this study, an adaptive neural-fuzzy analysis system is proposed for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data. Associated with enhanced active segment selection and wavelet-fractal features, adaptive fuzzy neural network (AFNN) is used for the recognition of left and right MI data. In addition to continuous wavelet transform (CWT) and Student’s two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further enhance the selection of active segments in the time-scale domain. Multiresolution fractal features are then extracted from wavelet data by using modified fractal dimension. Finally, fractal features are discriminated by AFNN clustering. The system is tested on two publicly available EEG datasets and compared with several popular approaches. The experimental results demonstrate that enhanced active segment selection can greatly improve the performance and AFNN clustering has a splendid potential in the applications of brain-computer interface (BCI) work.

[1]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[2]  James C. Bezdek,et al.  Fuzzy mathematics in pattern classification , 1973 .

[3]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[4]  Alex Pentland,et al.  Fractal-Based Description of Natural Scenes , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[6]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[7]  Miin-Shen Yang.  On a class of fuzzy classification maximum likelihood procedures , 1993 .

[8]  Miin-Shen Yang,et al.  On parameter estimation for normal mixtures based on fuzzy clustering algorithms , 1994 .

[9]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

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

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

[12]  V. Samar,et al.  Time–Frequency Analysis of Single-Sweep Event-Related Potentials by Means of Fast Wavelet Transform , 1999, Brain and Language.

[13]  G. R. Muller,et al.  Brain oscillations control hand orthosis in a tetraplegic , 2000, Neuroscience Letters.

[14]  G Pfurtscheller,et al.  Estimating the Mutual Information of an EEG-based Brain-Computer Interface , 2002, Biomedizinische Technik. Biomedical engineering.

[15]  Barak A. Pearlmutter,et al.  Linear Spatial Integration for Single-Trial Detection in Encephalography , 2002, NeuroImage.

[16]  P. Larsen,et al.  Fractal characteristics of breath to breath timing in sleeping infants , 2004, Respiratory Physiology & Neurobiology.

[17]  Gert Pfurtscheller,et al.  Characterization of four-class motor imagery EEG data for the BCI-competition 2005 , 2005, Journal of neural engineering.

[18]  K. Zou,et al.  Receiver-Operating Characteristic Analysis for Evaluating Diagnostic Tests and Predictive Models , 2007, Circulation.

[19]  G. Lightbody,et al.  A comparison of quantitative EEG features for neonatal seizure detection , 2008, Clinical Neurophysiology.

[20]  Yunong Zhang,et al.  Competitive Hopfield Network Combined With Estimation of Distribution for Maximum Diversity Problems , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Huijun Gao,et al.  Novel Robust Stability Criteria for Stochastic Hopfield Neural Networks With Time Delays , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Chen-Chia Chuang,et al.  Two-Stages Support Vector Regression for Fuzzy Neural Networks with Outliers , 2009 .

[23]  Wei-Yen Hsu,et al.  EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features , 2010, Journal of Neuroscience Methods.

[24]  Wei-Yen Hsu,et al.  Continuous EEG Signal Analysis for Asynchronous BCI Application , 2011, Int. J. Neural Syst..

[25]  Wei-Yen Hsu,et al.  EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier , 2011, Comput. Biol. Medicine.

[26]  Yong Feng Li,et al.  An Adaptive Fuzzy-Wavelet Method of Signal Processing for Fiber Optic Gyroscopes , 2011 .

[27]  S. Miyamoto.  Different Objective Functions in Fuzzy c-Means Algorithms and Kernel-Based Clustering , 2011 .

[28]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[29]  Reza Boostani,et al.  Lip Processing and Modeling based on Spatial Fuzzy Clustering in Color Images , 2011 .

[30]  WEI-YEN HSU,et al.  Analytic differential approach for robust registration of rat brain histological images , 2011, Microscopy research and technique.

[31]  Cheng-Jian Lin,et al.  Protein 3D HP Model Folding Simulation Using a Hybrid of Genetic Algorithm and Particle Swarm Optimization , 2011 .

[32]  P. Luukka.  A New Nonlinear Fuzzy Robust PCA Algorithm and Similarity Classifier in Classification of Medical Data Sets , 2011 .

[33]  Wei Yen Hsu,et al.  Application of Competitive Hopfield Neural Network to Brain-Computer Interface Systems , 2012, Int. J. Neural Syst..

[34]  Wei-Yen Hsu,et al.  Enhanced Active Segment Selection for Single-Trial EEG Classification , 2012, Clinical EEG and neuroscience.

[35]  Wei-Yen Hsu.  Registration Accuracy and Quality of Real-Life Images , 2012, PloS one.

[36]  Wei-Yen Hsu.  Application of Quantum-behaved Particle Swarm Optimization to Motor imagery EEG Classification , 2013, Int. J. Neural Syst..

[37]  Wei-Yen Hsu,et al.  Single-Trial Motor Imagery Classification using Asymmetry Ratio, phase Relation, Wavelet-Based Fractal, and their Selected Combination , 2013, Int. J. Neural Syst..

[38]  Wei-Yen Hsu,et al.  Independent Component Analysis and Multiresolution Asymmetry Ratio for Brain–Computer Interface , 2013, Clinical EEG and neuroscience.