Abstract
Brain–machine interfaces are systems that allow the control of a device such as a robot arm through a person’s brain activity; such devices can be used by disabled persons to enhance their life and improve their independence. This paper is an extended version of a work that aims at discriminating between left and right imagined hand movements using a support vector machine (SVM) classifier to control a robot arm in order to help a person to find an object in the environment. The main focus here is to search for the best features that describe efficiently the electroencephalogram data during such imagined gestures by comparing two feature extraction methods, namely the continuous wavelet transform (CWT) and the empirical modal decomposition (EMD), combined with the principal component analysis (PCA) that were fed through a linear and radial basis function (RBF) kernel SVM classifier. The experimental results showed high performance achieving an average accuracy across all the subjects of 92.75% with an RBF kernel SVM classifier using CWT and PCA compared to 80.25% accuracy obtained with EMD and PCA. The proposed system has been implemented and tested using data collected from five male subjects and it enabled the control of the robot arm in the right and the left direction.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alomari HM, Samaha A, AlKamha K (2013) Automated classification of L/R hand movement EEG signals using advanced feature extraction and machine learning. Int J Adv Comput Sci Appl 4:6
Bashashati A, Fatourechi M, Ward RK, Birch GE (2007) A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. J Neural Eng 4:R32–R57
Berger H (1929) Über das Elektrenkephalogramm des Menschen. Arch Psychiatr Nervenkr 87:527–570
Bousseta R, Tayeb S, El Ouakouak I et al (2016) EEG efficient classification of imagined hand movement using RBF kernel SVM. In: SITA 2016—11th international conference on intelligent systems: theories and applications
Cexus J-C (2005) Analyse des signaux non-stationnaires par transformation de Huang, Opérateur deTeager-Kaiser, et Transformation de Huang-Teager (THT)
Chang C-C, Lin C-J (2011) LIBSVM. ACM Trans Intell Syst Technol 2:1–27
Clynes M, Kline N (1960) Cyborgs and Space. Astronautics 26–27, 74–75
Decety J (1996) Do imagined and executed actions share the same neural substrate? Brain Res Cogn Brain Res 3:87–93
Dharmasena S, Lalitharathne K, Dissanayake K et al (2013) Online classification of imagined hand movement using a consumer grade EEG device. In: 2013 IEEE 8th international conference on industrial and information systems. IEEE, pp 537–541
Flandrin P, Gonçalvès P (2003) Sur la décomposition modale empirique. In: GRETSI. pp 149–152
Flórez F, Azorín JM, Iáñez E et al (2011) Development of a low-cost SVM-based spontaneous brain–computer interface. In: Madani K, Kacprzyk J, Filipe J (eds) {NCTA} 2011—proceedings of the international conference on neural computation theory and applications [part of the international joint conference on computational intelligence {IJCCI} 2011], Paris, France, 24–26 October, 2011. SciTePress, pp 415–421
Guger C, Harkam W, Hertnaes C, Pfurtscheller G (1999) Prosthetic control by an EEG-based brain- computer interface (BCI). In: AAATE 5th European conference for the advancement of assistive technology, pp 2–7
Hortal E, Planelles D, Costa A et al (2015) SVM-based brain–machine Interface for controlling a robot arm through four mental tasks. Neurocomputing 151:116–121
Hsu W-Y (2013) Independent component analysis and multiresolution asymmetry ratio for brain–computer interface. Clin EEG Neurosci 44:105–111
Hsu C-W, Chang C-C, Lin C-J (2003) A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University
Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A Math Phys Eng Sci 454:903–995
Kübler A, Mushahwar VK, Hochberg LR, Donoghue JP (2006) BCI Meeting 2005—workshop on clinical issues and applications. IEEE Trans Neural Syst Rehabil Eng 14:131–134
Lécuyer A, Lotte F, Reilly RB, et al (2008) Brain-computer interfaces, virtual reality, and videogames. Computer (Long Beach Calif) 41:66–72. doi:10.1109/MC.2008.410
Leeb R, Pfurtscheller G (2004) Walking through a virtual city by thought. In: The 26th annual international conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 4503–4506
Lotte F, Congedo M, Lécuyer A et al (2007) A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng 4:R1–R13
Mason SG, Birch GE (2003) A general framework for brain–computer interface design. IEEE Trans Neural Syst Rehabil Eng 11:70–85
Pang CCC, Upton ARM, Shine G, Kamath MV (2003) A comparison of algorithms for detection of spikes in the electroencephalogram. IEEE Trans Biomed Eng 50:521–526. doi:10.1109/TBME.2003.809479
Pfurtscheller G, Neuper C (2001) Motor imagery and direct brain–computer communication. Proc IEEE 89:1123–1134
Pfurtscheller G, Neuper C, Schlogl A, Lugger K (1998) Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans Rehabil Eng 6:316–325
Pfurtscheller G, Leeb R, Keinrath C et al (2006) Walking from thought. Brain Res 1071:145–152
Tayeb S, Mahmoudi A, Regragui F, Himmi MM (2014) Efficient detection of P300 using kernel PCA and support vector machine. In: 2014 second world conference on complex systems (WCCS). IEEE, pp 17–22
Vidal JJ (1973) Toward direct brain–computer communication. Annu Rev Biophys Bioeng 2:157–180
Wang F, Kim K, Wen S et al (2012) EEG based automatic left-right hand movement classification. In: 2012 24th Chinese control and decision conference (CCDC). IEEE, pp 1469–1472
Wolpaw JR, Birbaumer N, Mcfarland DJ et al (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113:767–791
Wolpaw JR, Loeb GE, Allison BZ et al (2006) BCI meeting 2005–workshop on signals and recording methods. IEEE Trans Neural Syst Rehabil Eng 14:138–141
Wu J, Wang J, Liu L (2006) Kernel-based method for automated walking patterns recognition using kinematics data. Springer, Berlin, pp 560–569
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bousseta, R., Tayeb, S., El Ouakouak, I. et al. EEG efficient classification of imagined right and left hand movement using RBF kernel SVM and the joint CWT_PCA. AI & Soc 33, 621–629 (2018). https://doi.org/10.1007/s00146-017-0749-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00146-017-0749-9