Abstract
A brain–computer interface (BCI) can be used for people with severe physical disabilities such as ALS, or amyotrophic lateral sclerosis. BCI can allow these individuals to communicate again by creating a new communication channel directly from the brain to an output device. BCI technology can allow paralyzed people to share their intent with others, and thereby demonstrate that direct communication from the brain to the external world is possible, and that it might serve useful functions. In this paper, we propose a system to exploit the P300 signal in the brain, a positive deflection in event-related potentials. The P300 signal can be incorporated into a spelling device. BCI systems include machine learning algorithms (MLA). Their performance depends on the feature extraction and classification techniques employed. This work discusses the performance of different machine learning algorithms. First, a preprocessing step is introduced to the subjects to extract the important features before applying the machine learning algorithms. The presented algorithms are linear discriminant analysis (LDA I and LDA II), support vector machine (SVM I, SVM II, SVM III, and SVM IV), linear regression (LREG), and Bayesian linear discriminant analysis (BLDA). It is found that BLDA and SVMIV classifiers yield the highest performance for both subjects considered in our study.
Similar content being viewed by others
References
Allison B (2003) P3 or not P3: toward a better P300 BCI. Ph.D. dissertation. University of California, SanDiego
ALS Association (2012) Quick Facts about ALS & The ALS Association. www.alsa.org/news/media/quick-facts.html
Azar AT, Balas VE, Olariu T (2014) Classification of EEG-based brain computer interfaces: advanced intelligent computational technologies and decision support systems studies in computational intelligence, vol 97. Springer, Berlin, p 106
Bennett KP, Campbell C (2000) Support vector machines: type. Explor Newslett 2:1
Blankertz B, Curio G, Müller KR (2002) Classifying single trial EEG: towards brain computer interfacing. In: Advances in neural information processing systems (NIPS 01), vol 14, p 157
Blankertz B, Kawanabe M, Tomioka R, Hohlefeld F, Müller K-R, Nikulin VV (2007) Invariant common spatial patterns: alleviating nonstationarities in brain-computer interfacing. In: Advances in neural information processing systems, pp 113–120
Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Knowl Discov Data Min 2:121
Carlson T, Millán JR (2013) Brain-controlled wheelchairs: a robotic architecture. IEEE Robot Autom Mag 20:65
Cecotti H, Graser A (2011) Convolutional neural networks for P300 detection with application to brain-computer interfaces. IEEE Trans Commun 33:433
Donchin E, Spencer KM, Wijensinghe R (2000) The mental prosthesis: assessing the speed of a P300-based brain computer interface. IEEE Trans 8:174
Duda RO, Hart PE, Stork DG (2001) Pattern recognition, 2nd edn. Wiley-Interscience, New York
Farwell LA, Donchin E (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70:510
Ferrez PW, Millan JR (2008) Error-related EEG potentials generated during simulated brain computer interaction. IEEE Trans Biomed Eng 55:923
Fouad IA, Hadidi T (2014) Classifying brain-computer interface features based on statistics and density of power spectrum. Int J Biomed Eng Technol 18:1–13
Fouad IA, Labib FE-ZM (2017) Attempts towards the first brain computer interface (BCI) system in INAYA medical college. Int J Comput Appl Technol Inderscience 55:2
Fukunaga K (1990) Statistical pattern recognition, 2nd edn. Academic Press Inc, New York
Garrett D, Peterson DA, Anderson CW, Thaut MH (2003) Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans Neural Syst Rehabil Eng 11:141
Geng T, Dyson M, Tsui CS, Gan JQ (2007) A 3-class asynchronous BCI for controlling mobile robots. In: MAIA BCI workshop—BCI meets robotics: challenging issues in brain computer interaction and shared control, Leuven
Hohffmann U, Garcia G, Vesin J-M, Ebrahimi T (2004) Application of the evidence framework to brain computer interfaces. IEEE Eng Med Biol Conf 1:446
Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22:4
Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain computer interfaces. J Neural Eng 4:R1
Lu J, Speier W, Hu X, Pouratian N (2013) The effects of stimulus timing features on P300 speller performance. Clin Neurophysiol 124:306
Mandel C, Luth T, Laue T, Rofer T, Graser A, Krieg-Bruckner B (2009) Navigating a smart wheelchair with a brain computer interface interpreting steady-state visual evoked potentials. In: Intelligent robots and systems: IEEE/RSJ international conference on IEEE, pp 1118–1125
Matlab (2017) Matlab version 9.2.0.556344 (R2017a) and its signal processing toolbox
Mattout J, Perrin M, Bertrand O, Maby E (2014) Update article: improving BCI performance through co-adaptation: applications to the P300-speller. Ann Phys Rehabil Med 58:23–28
McCane L, Heckman S, McFarland D, Townsend G, Mak J, Sellers E, Zeitlin D, Tenteromano L, Wolpaw J, Vaughan T (2015) P300-based brain-computer interface (BCI) event-related potentials (ERPs): people with amyotrophic lateral sclerosis (ALS) vs. age matched controls. Clin Neurophysiol 126:2124–2131
McFarland DJ, Anderson CW, Muller K, Schlogl A, Krusienski DJ (2006) Bci meeting 2005 workshop on BCI signal processing: feature extraction and translation. IEEE Trans Neural Syst Rehabil Eng 14:135
Moore MM (2003) Real-world applications for brain-computer interface technology. IEEE Trans Neural Syst Rehabil Eng 11:162
Nicolas Alonso LF, Gomez-Gil J (2012) Brain computer interfaces, a review. Sensors 12:1211
Rakotomamonjy A, Guigue V (2008) BCI competition III: dataset II—ensemble of SVMs for BCI P300 speller: IEEE Trans. Biomed Eng 55:1147
Rakotomamonjy A, Guigue V, Mallet G, Alvarado V (2005) Ensemble of SVMs for improving brain computer interface P300 speller performances. In: International conference on artificial neural networks
Ramoser H, Muller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 8:441
Rebsamen B (2009) A brain controlled wheelchair to navigate in familiar environments. Ph.D. dissertation. National University of Singapore
Teplan M (2002) Fundamentals of EEG measurement. Meas Sci Rev 2:1
Townsend G, LaPallo B, Boulay C, Krusienski D, Frye G, Hauser C, Schwartz N, Vaughan T, Wolpaw J, Sellers E (2010) A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin Neurophysiol 121:1109
Tsui CSL, Gan JQ, Hu H (2011) A self-paced motor imagery based brain computer interface for robotic wheelchair control. J Clin EEG Neurosci 42:225
Vidaurre C, Sannelli C, Müller K-R, Blankertz B (2011) Machine-learning-based co-adaptive calibration for brain computer interfaces. Neural Comput 23:791
Waldert S, Pistohl T, Braun C, Ball T, Aertsen A, Mehring C (2009) A review on directional information in neural signals for brain-machine interfaces. J Physiol Paris 103:244
Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain computer interfaces for communication and control. Clin Neurophysiol 113:767
Wolpaw JR, McFarland DJ, Vaughan TM, Schalk G (2003) The wadsworth center brain computer interface (BCI) research and development program. IEEE Trans Neural Syst Rehabil Eng 11:1
Wolpaw JR, Schalk G, Krusienski D (2004) Wadsworth Center, NYS Department of Health. https://bbci.de/competition/iii/
Wu Z (2014) Studying modulation on simultaneously activated SSVEP neural networks by a cognitive task. J Biol Phys 1:55
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Fouad, I.A., Labib, F.EZ.M., Mabrouk, M.S. et al. Improving the performance of P300 BCI system using different methods. Netw Model Anal Health Inform Bioinforma 9, 64 (2020). https://doi.org/10.1007/s13721-020-00268-1
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13721-020-00268-1