Classifying three imaginary states of the same upper extremity using time-domain features

Brain-computer interface (BCI) allows collaboration between humans and machines. It translates the electrical activity of the brain to understandable commands to operate a machine or a device. In this study, we propose a method to improve the accuracy of a 3-class BCI using electroencephalographic (EEG) signals. This BCI discriminates rest against imaginary grasps and elbow movements of the same limb. This classification task is challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area. The proposed method extracts time-domain features and classifies them using a support vector machine (SVM) with a radial basis kernel function (RBF). An average accuracy of 74.2% was obtained when using the proposed method on a dataset collected, prior to this study, from 12 healthy individuals. This accuracy was higher than that obtained when other widely used methods, such as common spatial patterns (CSP), filter bank CSP (FBCSP), and band power methods, were used on the same dataset. These results are encouraging and the proposed method could potentially be used in future applications including BCI-driven robotic devices, such as a portable exoskeleton for the arm, to assist individuals with impaired upper extremity functions in performing daily tasks.

[1]  S. Salleh,et al.  Neural network-based three-class motor imagery classification using time-domain features for BCI applications , 2014, 2014 IEEE REGION 10 SYMPOSIUM.

[2]  James R. Carey,et al.  Within-limb somatotopy in primary motor cortex – revealed using fMRI , 2010, Cortex.

[3]  Charles W. Anderson,et al.  Classification of EEG Signals from Four Subjects During Five Mental Tasks , 2007 .

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

[5]  P. Geethanjali,et al.  Time domain Feature extraction and classification of EEG data for Brain Computer Interface , 2012, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery.

[6]  Arnaud Delorme,et al.  EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing , 2011, Comput. Intell. Neurosci..

[7]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b , 2012, Front. Neurosci..

[8]  Dante Mantini,et al.  Estimating a neutral reference for electroencephalographic recordings: the importance of using a high-density montage and a realistic head model , 2015, Journal of neural engineering.

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

[10]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[11]  J. Binder,et al.  Functional magnetic resonance imaging of complex human movements , 1993, Neurology.

[12]  M Hamedi,et al.  Robust classification of motor imagery EEG signals using statistical time–domain features , 2013, Physiological measurement.

[13]  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.

[14]  Brendan Z. Allison,et al.  Brain-Computer Interfaces , 2010 .

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

[16]  Omar Farooq,et al.  Classification of Wrist Movements Using EEG Signals , 2013 .

[17]  Á. Gil-Agudo,et al.  Continuous decoding of movement intention of upper limb self-initiated analytic movements from pre-movement EEG correlates , 2014, Journal of NeuroEngineering and Rehabilitation.

[18]  M. Schuurmans,et al.  Task-oriented training in rehabilitation after stroke: systematic review. , 2009, Journal of advanced nursing.

[19]  M. Hamedi,et al.  Multiclass self-paced motor imagery temporal features classification using least-square support vector machine , 2014, 2014 IEEE 19th International Functional Electrical Stimulation Society Annual Conference (IFESS).

[20]  Klaus-Robert Müller,et al.  Combining Features for BCI , 2002, NIPS.

[21]  G Pfurtscheller,et al.  Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[22]  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.

[23]  V. Vapnik.  The Support Vector Method of Function Estimation , 1998 .

[24]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[25]  Cuntai Guan,et al.  Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs , 2012, Pattern Recognit..

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

[27]  Cuntai Guan,et al.  Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[28]  Jason Weston,et al.  A user's guide to support vector machines. , 2010, Methods in molecular biology.

[29]  Wei Duan,et al.  Improved Efficacy and Reduced Toxicity of Doxorubicin Encapsulated in Sulfatide-Containing Nanoliposome in a Glioma Model , 2014, PloS one.

[30]  Michel Verleysen,et al.  Feature Selection for Interpatient Supervised Heart Beat Classification , 2011, BIOSIGNALS.

[31]  Oliviero Carugo,et al.  Data Mining Techniques for the Life Sciences , 2009, Methods in Molecular Biology.

[32]  F. Sepulveda,et al.  A Comparison of Time, Frequency and ICA Based Features and Five Classifiers for Wrist Movement Classification in EEG Signals , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[33]  Yijun Wang,et al.  Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[34]  Dario Farina,et al.  Single-trial discrimination of type and speed of wrist movements from EEG recordings , 2009, Clinical Neurophysiology.

[35]  S. Tseng,et al.  Evaluation of parametric methods in EEG signal analysis. , 1995, Medical engineering & physics.

[36]  Aleksandra Vučković,et al.  A two-stage four-class BCI based on imaginary movements of the left and the right wrist. , 2012, Medical engineering & physics.

[37]  A. Geurts,et al.  Definition dependent properties of the cortical silent period in upper-extremity muscles, a methodological study , 2014, Journal of NeuroEngineering and Rehabilitation.

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

[39]  David M. Meyer,et al.  A distant space thermometer , 1994, Nature.

[40]  M. Hallett,et al.  Rapid plasticity of human cortical movement representation induced by practice. , 1998, Journal of neurophysiology.

[41]  T.M. McGinnity,et al.  Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[42]  K.-R. Muller,et al.  BCI meeting 2005-workshop on BCI signal processing: feature extraction and translation , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[43]  D. Tucker.  Spatial sampling of head electrical fields: the geodesic sensor net. , 1993, Electroencephalography and clinical neurophysiology.

[44]  Stephen J. Roberts,et al.  A self-paced brain–computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training , 2009, Medical & Biological Engineering & Computing.

[45]  J. Mazziotta,et al.  Mapping motor representations with positron emission tomography , 1994, Nature.

[46]  M. J. E. Salami,et al.  Evaluation of time-domain features for motor imagery movements using FCM and SVM , 2012, 2012 Ninth International Conference on Computer Science and Software Engineering (JCSSE).

[47]  Carlo Menon,et al.  Assisting drinking with an affordable BCI-controlled wearable robot and electrical stimulation: a preliminary investigation , 2014, Journal of NeuroEngineering and Rehabilitation.

[48]  Klaus-Robert Müller,et al.  Optimizing spatio-temporal filters for improving Brain-Computer Interfacing , 2005, NIPS.

[49]  Zhongming Liu,et al.  An enhanced time-frequency-spatial approach for motor imagery classification , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[50]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[51]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[52]  J. Donoghue,et al.  Shared neural substrates controlling hand movements in human motor cortex. , 1995, Science.

[53]  G. Pfurtscheller,et al.  Using adaptive autoregressive parameters for a brain-computer-interface experiment , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

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

[55]  Christian Seifert,et al.  Single-trial coupling of EEG and fMRI reveals the involvement of early anterior cingulate cortex activation in effortful decision making , 2008, NeuroImage.

[56]  Bin He,et al.  Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms , 2015, Proceedings of the IEEE.

[57]  Gert Pfurtscheller,et al.  SUBJECT SPECIFIC EEG PATTERNS DURING MOTOR IMAGINARY , 1997 .

[58]  L. Carey,et al.  Task-specific training: evidence for and translation to clinical practice. , 2009, Occupational therapy international.

[59]  Philip H. Ramsey.  Nonparametric Statistical Methods , 1974, Technometrics.

[60]  K. Jellinger.  Toward Brain-Computer Interfacing , 2009 .

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