Brain–Computer Interface Classifier for Wheelchair Commands Using Neural Network With Fuzzy Particle Swarm Optimization

This paper presents the classification of a three-class mental task-based brain-computer interface (BCI) that uses the Hilbert-Huang transform for the features extractor and fuzzy particle swarm optimization with cross-mutated-based artificial neural network (FPSOCM-ANN) for the classifier. The experiments were conducted on five able-bodied subjects and five patients with tetraplegia using electroencephalography signals from six channels, and different time-windows of data were examined to find the highest accuracy. For practical purposes, the best two channel combinations were chosen and presented. The three relevant mental tasks used for the BCI were letter composing, arithmetic, and Rubik's cube rolling forward, and these are associated with three wheelchair commands: left, right, and forward, respectively. An additional eyes closed task was collected for testing and used for on-off commands. The results show a dominant alpha wave during eyes closure with average classification accuracy above 90%. The accuracies for patients with tetraplegia were lower compared to the able-bodied subjects; however, this was improved by increasing the duration of the time-windows. The FPSOCM-ANN provides improved accuracies compared to genetic algorithm-based artificial neural network (GA-ANN) for three mental tasks-based BCI classifications with the best classification accuracy achieved for a 7-s time-window: 84.4% (FPSOCM-ANN) compared to 77.4% (GA-ANN). More comparisons on feature extractors and classifiers were included. For two-channel classification, the best two channels were O1 and C4, followed by second best at P3 and O2, and third best at C3 and O2. Mental arithmetic was the most correctly classified task, followed by mental Rubik's cube rolling forward and mental letter composing.

[1]  Robert Oostenveld,et al.  MATLAB-Based Tools for BCI Research , 2010, Brain-Computer Interfaces.

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

[3]  Hung T. Nguyen,et al.  Toward fewer EEG channels and better feature extractor of non-motor imagery mental tasks classification for a wheelchair thought controller , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Yan Guozheng,et al.  EEG feature extraction based on wavelet packet decomposition for brain computer interface , 2008 .

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

[6]  Leontios J. Hadjileontiadis,et al.  A Novel Emotion Elicitation Index Using Frontal Brain Asymmetry for Enhanced EEG-Based Emotion Recognition , 2011, IEEE Transactions on Information Technology in Biomedicine.

[7]  Dean J. Krusienski,et al.  BCI Signal Processing: Feature Translation , 2012 .

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

[9]  R. Palaniappan,et al.  Identifying Individuality Using Mental Task Based Brain Computer Interface , 2005, 2005 3rd International Conference on Intelligent Sensing and Information Processing.

[10]  Hung T. Nguyen,et al.  Intelligent technologies for real-time biomedical engineering applications , 2008, Int. J. Autom. Control..

[11]  Hong Bao,et al.  GPGPU-Aided Ensemble Empirical-Mode Decomposition for EEG Analysis During Anesthesia , 2010, IEEE Transactions on Information Technology in Biomedicine.

[12]  Brendan Z. Allison,et al.  Could Anyone Use a BCI? , 2010, Brain-Computer Interfaces.

[13]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[14]  Maria Q. Feng,et al.  A technique to improve the empirical mode decomposition in the Hilbert-Huang transform , 2003 .

[15]  Ram Bilas Pachori,et al.  Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition , 2012, IEEE Transactions on Information Technology in Biomedicine.

[16]  Gernot R. Müller-Putz,et al.  Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic , 2007, Comput. Intell. Neurosci..

[17]  Brendan Z. Allison,et al.  Is It Significant? Guidelines for Reporting BCI Performance , 2012 .

[18]  M. Nuttin,et al.  A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots , 2008, Clinical Neurophysiology.

[19]  J.P. Donoghue,et al.  BCI meeting 2005-workshop on clinical issues and applications , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  Moritz Grosse-Wentrup,et al.  Critical issues in state-of-the-art brain–computer interface signal processing , 2011, Journal of neural engineering.

[21]  Hung T. Nguyen,et al.  Intelligent fuzzy particle swarm optimization with cross-mutated operation , 2012, 2012 IEEE Congress on Evolutionary Computation.

[22]  Gerwin Schalk,et al.  Brain–computer symbiosis , 2008, Journal of neural engineering.

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

[24]  Konstantinos N. Plataniotis,et al.  Face recognition using LDA-based algorithms , 2003, IEEE Trans. Neural Networks.

[25]  Hung T. Nguyen,et al.  Mental non-motor imagery tasks classifications of brain computer interface for wheelchair commands using genetic algorithm-based neural network , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[26]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[27]  Klaus-Robert Müller,et al.  The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States , 2006, J. Univers. Comput. Sci..

[28]  M. Stokes,et al.  Cognitive tasks for driving a brain-computer interfacing system: a pilot study , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[29]  J. Wolpaw,et al.  Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface , 2005, Neurology.

[30]  M. Nuttin,et al.  Asynchronous non-invasive brain-actuated control of an intelligent wheelchair , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[31]  Sally I. McClean,et al.  Combining Wavelet Analysis and Bayesian Networks for the Classification of Auditory Brainstem Response , 2006, IEEE Transactions on Information Technology in Biomedicine.

[32]  D. Craig,et al.  Adaptive EEG Thought Pattern Classifier for Advanced Wheelchair Control , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[33]  G. Birch,et al.  Initial on-line evaluations of the LF-ASD brain-computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[34]  M. Conson,et al.  Selective motor imagery defect in patients with locked-in syndrome , 2008, Neuropsychologia.

[35]  A. Craig,et al.  The effectiveness of a hands-free environmental control system for the profoundly disabled. , 2002, Archives of physical medicine and rehabilitation.

[36]  Michael L. Boninger,et al.  Toward Synergy-Based Brain-Machine Interfaces , 2011, IEEE Transactions on Information Technology in Biomedicine.

[37]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[38]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[39]  Xiaojun Wu,et al.  Graph Regularized Nonnegative Matrix Factorization for Data Representation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Farhad Faradji,et al.  Design of a mental task-based brain-computer interface with a zero false activation rate using very few EEG electrode channels , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[41]  Bernhard Schölkopf,et al.  A Review of Performance Variations in SMR-Based Brain−Computer Interfaces (BCIs) , 2013 .

[42]  Thomas S. Huang,et al.  Graph Regularized Nonnegative Matrix Factorization for Data Representation. , 2011, IEEE transactions on pattern analysis and machine intelligence.

[43]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[44]  Elif Derya Übeyli,et al.  Multiclass Support Vector Machines for EEG-Signals Classification , 2007, IEEE Trans. Inf. Technol. Biomed..