EEG-Based Brain-Computer Interface for Decoding Motor Imagery Tasks within the Same Hand Using Choi-Williams Time-Frequency Distribution

This paper presents an EEG-based brain-computer interface system for classifying eleven motor imagery (MI) tasks within the same hand. The proposed system utilizes the Choi-Williams time-frequency distribution (CWD) to construct a time-frequency representation (TFR) of the EEG signals. The constructed TFR is used to extract five categories of time-frequency features (TFFs). The TFFs are processed using a hierarchical classification model to identify the MI task encapsulated within the EEG signals. To evaluate the performance of the proposed approach, EEG data were recorded for eighteen intact subjects and four amputated subjects while imagining to perform each of the eleven hand MI tasks. Two performance evaluation analyses, namely channel- and TFF-based analyses, are conducted to identify the best subset of EEG channels and the TFFs category, respectively, that enable the highest classification accuracy between the MI tasks. In each evaluation analysis, the hierarchical classification model is trained using two training procedures, namely subject-dependent and subject-independent procedures. These two training procedures quantify the capability of the proposed approach to capture both intra- and inter-personal variations in the EEG signals for different MI tasks within the same hand. The results demonstrate the efficacy of the approach for classifying the MI tasks within the same hand. In particular, the classification accuracies obtained for the intact and amputated subjects are as high as 88.8% and 90.2%, respectively, for the subject-dependent training procedure, and 80.8% and 87.8%, respectively, for the subject-independent training procedure. These results suggest the feasibility of applying the proposed approach to control dexterous prosthetic hands, which can be of great benefit for individuals suffering from hand amputations.

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

[2]  A. Flisberg,et al.  Automatic classification of background EEG activity in healthy and sick neonates , 2010, Journal of neural engineering.

[3]  Gernot R. Müller-Putz,et al.  Control of an Electrical Prosthesis With an SSVEP-Based BCI , 2008, IEEE Transactions on Biomedical Engineering.

[4]  Joerg F. Hipp,et al.  Time-Frequency Analysis , 2014, Encyclopedia of Computational Neuroscience.

[5]  Paolo Castiglioni Choi–Williams Distribution , 2005 .

[6]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[7]  John M. O'Toole,et al.  Time-Frequency Processing of Nonstationary Signals: Advanced TFD Design to Aid Diagnosis with Highlights from Medical Applications , 2013, IEEE Signal Processing Magazine.

[8]  Boualem Boashash,et al.  Principles of time-frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection , 2015, Pattern Recognit..

[9]  Bin He,et al.  EEG Control of a Virtual Helicopter in 3-Dimensional Space Using Intelligent Control Strategies , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Jonathan R Wolpaw,et al.  A brain-computer interface for long-term independent home use , 2010, Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases.

[11]  Dean J Krusienski,et al.  Brain-computer interfaces in medicine. , 2012, Mayo Clinic proceedings.

[12]  U. Rajendra Acharya,et al.  Application of entropies for automated diagnosis of epilepsy using EEG signals: A review , 2015, Knowl. Based Syst..

[13]  Trent J. Bradberry,et al.  Reconstructing Three-Dimensional Hand Movements from Noninvasive Electroencephalographic Signals , 2010, The Journal of Neuroscience.

[14]  Zhilin Zhang,et al.  Evolving Signal Processing for Brain–Computer Interfaces , 2012, Proceedings of the IEEE.

[15]  S. Hahn Hilbert Transforms in Signal Processing , 1996 .

[16]  Francisco Sepulveda,et al.  Delta band contribution in cue based single trial classification of real and imaginary wrist movements , 2008, Medical & Biological Engineering & Computing.

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

[18]  M. Peters,et al.  Volume conduction effects in EEG and MEG. , 1998, Electroencephalography and clinical neurophysiology.

[19]  Somaya Al-Máadeed,et al.  On the use of time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[21]  Scott T. Grafton,et al.  Localization of grasp representations in humans by positron emission tomography , 1996, Experimental Brain Research.

[22]  Bangyan Zhou,et al.  A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface , 2016, PloS one.

[23]  W. De Clercq,et al.  Automatic Removal of Ocular Artifacts in the EEG without an EOG Reference Channel , 2006, Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006.

[24]  Anant Madabhushi,et al.  Cascaded multi-class pairwise classifier (CascaMPa) for normal, cancerous, and cancer confounder classes in prostate histology , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[25]  Qing Yang,et al.  A Brain-Computer Interface Based on a Few-Channel EEG-fNIRS Bimodal System , 2017, IEEE Access.

[26]  Chao Li,et al.  A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control , 2016, Sensors.

[27]  A. Doud,et al.  Continuous Three-Dimensional Control of a Virtual Helicopter Using a Motor Imagery Based Brain-Computer Interface , 2011, PloS one.

[28]  Manfredo Atzori,et al.  Electromyography data for non-invasive naturally-controlled robotic hand prostheses , 2014, Scientific Data.

[29]  Bin He,et al.  EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks , 2016, IEEE Transactions on Biomedical Engineering.

[30]  G. Pfurtscheller,et al.  Graz-BCI: state of the art and clinical applications , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[31]  Hans-Jochen Heinze,et al.  Single trial discrimination of individual finger movements on one hand: A combined MEG and EEG study , 2012, NeuroImage.

[32]  Ke Liao,et al.  Decoding Individual Finger Movements from One Hand Using Human EEG Signals , 2014, PloS one.

[33]  Eli M. Mizrahi,et al.  A Multi-stage System for the Automated Detection of Epileptic Seizures in Neonatal EEG , 2009 .

[34]  N. Birbaumer Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. , 2006, Psychophysiology.

[35]  Boualem Boashash,et al.  Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study , 2016, Knowl. Based Syst..

[36]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .

[37]  LJubisa Stankovic,et al.  A measure of some time-frequency distributions concentration , 2001, Signal Process..

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

[39]  Ulrich H.-G. Kreßel,et al.  Pairwise classification and support vector machines , 1999 .

[40]  Carlo Menon,et al.  EEG Classification of Different Imaginary Movements within the Same Limb , 2015, PloS one.

[41]  Carlos Guerrero-Mosquera,et al.  New feature extraction approach for epileptic EEG signal detection using time-frequency distributions , 2010, Medical & Biological Engineering & Computing.

[42]  Tian Lan,et al.  Salient EEG Channel Selection in Brain Computer Interfaces by Mutual Information Maximization , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[43]  Mohammed Imamul Hassan Bhuiyan,et al.  Detection of Seizure and Epilepsy Using Higher Order Statistics in the EMD Domain , 2013, IEEE Journal of Biomedical and Health Informatics.

[44]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[45]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[46]  M. Jeannerod Mental imagery in the motor context , 1995, Neuropsychologia.

[47]  William J. Williams,et al.  Improved time-frequency representation of multicomponent signals using exponential kernels , 1989, IEEE Trans. Acoust. Speech Signal Process..

[48]  Lei Ding,et al.  Motor imagery classification by means of source analysis for brain–computer interface applications , 2004, Journal of neural engineering.

[49]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[50]  Mj Martin Bastiaans Time-frequency signal analysis , 2008 .

[51]  K. Lafleur,et al.  Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface , 2013, Journal of neural engineering.

[52]  S. M. Debbal,et al.  Time-frequency analysis of the first and the second heartbeat sounds , 2007, Appl. Math. Comput..

[53]  Pablo M. Granitto,et al.  Cascade classifiers for multiclass problems , 2005 .

[54]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[55]  Ruimin Wang,et al.  Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography , 2014, PloS one.

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

[57]  Mohammed Imamul Hassan Bhuiyan,et al.  On the classification of sleep states by means of statistical and spectral features from single channel Electroencephalogram , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[58]  Boualem Boashash,et al.  Time-Frequency Signal Analysis and Processing: A Comprehensive Reference , 2015 .

[59]  Rami Alazrai,et al.  Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation , 2017 .

[60]  Niels Birbaumer,et al.  fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment , 2007, Comput. Intell. Neurosci..

[61]  G. Buccino,et al.  Action observation versus motor imagery in learning a complex motor task: A short review of literature and a kinematics study , 2013, Neuroscience Letters.

[62]  Kalyana Chakravarthy Veluvolu,et al.  Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner , 2017, Sensors.

[63]  Francisco Sepulveda,et al.  Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface , 2008, Inf. Sci..

[64]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[65]  Michael I. Jordan,et al.  On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.

[66]  Christa Neuper,et al.  An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate , 2004, IEEE Transactions on Biomedical Engineering.

[67]  U. Rajendra Acharya,et al.  Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads , 2016, Knowl. Based Syst..

[68]  Xingyu Wang,et al.  Optimized Motor Imagery Paradigm Based on Imagining Chinese Characters Writing Movement , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[69]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[70]  Zhiquan Wang,et al.  Recognition of human activities using SVM multi-class classifier , 2010, Pattern Recognit. Lett..