Abstract
Motor imagery (MI) related Electroencephalogram (EEG) signal classification is one of the main challenge in designing a brain computer interface (BCI) system. Linear Discriminant Analysis (LDA) has a very low computational requirement which makes it suitable for online BCI system. This paper proposes an advanced and simple classification technique for MI related BCI system. Initially the signal is extracted for different features. The LDA classifier has been used to propose technique to design an MI based BCI. For contrastive comparison other classification techniques have been evaluated by classification accuracy and Cohen’s kappa.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ahsan, M.R., Ibrahimy, M.I., Khalifa, O.O.: EMG signal classification for human computer interaction: A review. Eur. J. Sci. Res., 3, 480–501 (2009)
Shahid, S., Prasad, G.: Bispectrum based feature extraction technique for devising a practical Brain-Computer Interface. J. Neural Eng., 8(2), Article Number: 025014 (2011)
Wolpaw, J.R., Birbaumer, N., McFarland, D.J, Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin Neurophysiol, 113(6):767–791, (2002)
Brunner, C., Leeb, R., Mü,ller-Putz, G., Schló,gl, A., Pfurtscheller, G.: BCI Competition 2008, Graz Data Set a, Laboratory of Brain-Computer Interfaces, Inst. for Knowledge Discovery, Graz Univ. of Technology (2008)
Rathinave, S., Arumugam, S.: Full Shoe Print Recognition based on Pass Band DCT and Partial Shoe Print Identification using Overlapped Block Method for Degraded Images. International Journal of Computer Applications (0975 – 8887), vol. 26 – No.8 (2011)
Garrett, D., Peterson, D.A., Anderson, C.W., Thaut, M.H.: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Transactionson Neural System and Rehabilitation Engineering, vol. 11, pp. 141-144 (2003)
Hasan, M.R., Ibrahimy, M.I., Motakabber, S.M.A.: Performance Analysis of Different Techniques for Brain Computer Interfacing. International Conference on Computing, Electrical and Electronic Engineering (ICCEEE), pp. 730 – 734 (2013)
Sim, J., Wright, C.C.: The kappa statistic in reliability studies: Use, interpretation, and sample size requirements. Physical Therapy, vol. 85(3), pp. 257–268 (2005)
Acknowledgment
This research has been supported by the Ministry of Higher Education of Malaysia through the Exploratory Research Grant Scheme ERGS12-026-0026.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hasan, M.R., Ibrahimy, M.I., Motakabber, S.M.A., Shahid, S. (2015). Classification of Multichannel EEG Signal by Linear Discriminant Analysis. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-08422-0_42
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08421-3
Online ISBN: 978-3-319-08422-0
eBook Packages: EngineeringEngineering (R0)