Multi-objective binary DE algorithm for optimizing the performance of Devanagari script-based P300 speller

• Autorzy: Chaurasiya R. K. , Londhe N. D. , Ghosh S.

Identyfikatory

DOI

10.1016/j.bbe.2017.04.006

• Języki publikacji: EN

Abstrakty

EN

P300 speller-based brain-computer interface (BCI) allows a person to communicate with a computer using only brain signals. In order to achieve better reliability and user continence, it is desirable to have a system capable of providing accurate classification with as few EEG channels as possible. This article proposes an approach based on multi-objective binary differential evolution (MOBDE) algorithm to optimize the system accuracy and number of EEG channels used for classification. The algorithm on convergence provides a set of pareto-optimal solutions by solving the trade-off between the classification accuracy and the number of channels for Devanagari script (DS)-based P300 speller system. The proposed method is evaluated on EEG data acquired from 9 subjects using a 64 channel EEG acquisition device. The statistical analysis carried out in the article, suggests that the proposed method not only increases the classification accuracy but also increases the over-all system reliabil-ity in terms of improved user-convenience and information transfer rate (ITR) by reducing the EEG channels. It was also revealed that the proposed system with only 16 channels was able to achieve higher classification accuracy than a system which uses all 64 channel's data for feature extraction and classification.

Słowa kluczowe

EN

brain computer interface; Devanagari script; multiobjective optimization; binary DE; P300 speller; support vector machine

PL

interfejs mózg-komputer; optymalizacja wielokryterialna; P300; maszyna wektorów wspierających

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2017
• Tom: Vol. 37, no. 3
• Strony: 422--431
• Opis fizyczny: Bibliogr. 38 poz., rys., tab., wykr.

Twórcy

autor

Chaurasiya R. K.

• Department of Electronics and Telecommunication Engineering, National Institute of Technology, Raipur, Raipur-C.G, PIN-492010, India

autor

Londhe N. D.

• Department of Electrical Engineering, National Institute of Technology, Raipur, India

autor

Ghosh S.

• Department of Electrical Engineering, National Institute of Technology, Raipur, India

Bibliografia

• [1] Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 1988 Dec;70:510–23.
• [2] Akcakaya M, Peters B, Moghadamfalahi M, Mooney AR, Orhan U, Oken B, et al. Noninvasive brain-computer interfaces for augmentative and alternative communication. IEEE Rev Biomed Eng 2014;7:31–49.
• [3] Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol 2002;113:767–91.
• [4] Allison BZ, Pineda JA. ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system. Neural Systems and Rehabilitation Engineering IEEE Transactions on 2003;11:110–3.
• [5] Sellers EW, Krusienski DJ, McFarland DJ, Vaughan TM, Wolpaw JR. A P300 event-related potential brain–computer interface (BCI): the effects of matrix size and inter stimulus interval on performance. Biol Psychol 2006;73:242–52.
• [6] Allison BZ, Pineda JA. Effects of SOA and flash pattern manipulations on ERPs, performance, and preference: implications for a BCI system. Int J Psychophysiol 2006;59:127–40.
• [7] Salvaris M, Sepulveda F. Visual modifications on the P300 speller BCI paradigm. J Neural Eng 2009;6:046011.
• [8] Kaper M, Meinicke P, Grossekathoefer U, Lingner T, Ritter H. BCI Competition 2003–Data set IIb: support vector machines for the P300 speller paradigm. IEEE Trans Biomed Eng 2004;51:1073–6.
• [9] Hoffmann U, Vesin JM, Ebrahimi T, Diserens K. An efficient P300-based brain-computer interface for disabled subjects. J Neurosci Methods 2008;167:115–25.
• [10] Krusienski DJ, Sellers EW, Cabestaing F, Bayoudh S, McFarland DJ, Vaughan TM, et al. A comparison of classification techniques for the P300 Speller. J Neural Eng 2006;3:299–305.
• [11] Manyakov NV, Chumerin N, Combaz A, Van Hulle MM. Comparison of classification methods for P300 brain-computer interface on disabled subjects. Comput Intell Neurosci 2011;2011:519868.
• [12] Cecotti H. Toward shift invariant detection of event-related potentials in non-invasive brain-computer interface. Pattern Recognition Letters 2015;66:127–34.
• [13] Chaurasiya RK, Londhe ND, Ghosh S. An efficient P300 speller system for Brain-Computer Interface. Signal Processing, Computing and Control (ISPCC), 2015 International Conference on. 2015. pp. 57–62.
• [14] Bhatnagar V, Yede N, Keram RS, Chaurasiya R. A modified approach to ensemble of SVM for P300 based brain computer interface. 2016 International Conference on Advances in Human Machine Interaction (HMI). 2016. pp. 1–5.
• [15] Chaurasiya RK, Londhe ND, Ghosh S. Binary DE-Based Channel Selection and Weighted Ensemble of SVM Classification for Novel Brain–Computer Interface Using Devanagari Script-Based P300 Speller Paradigm. Int J Human–Comput Interaction 2016;1–17.
• [16] Guan C, Thulasidas M, Wu J. High performance P300 speller for brain-computer interface. Biomedical Circuits and Systems, 2004 IEEE International Workshop on; 2004. pp. S3/5/INV-S3/13-16.
• [17] Fazel-Rezai R, Abhari K. A region-based P300 speller for brain-computer interface. Canadian J Electr Comput Eng 2009;34:81–5.
• [18] Townsend G, LaPallo B, Boulay C, Krusienski D, Frye G, Hauser C, et al. A novel P300-based brain–computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin Neurophysiol 2010;121:1109–20.
• [19] Blankertz B, Losch F, Krauledat M, Dornhege G, Curio G, Muller KR. The Berlin Brain–Computer Interface: accurate performance from first-session in BCI-naive subjects. IEEE Trans Biomed Eng 2008 Oct;55:2452–62.
• [20] Xu M, Qi H, Ma L, Sun C, Zhang L, Wan B, et al. Channel selection based on phase measurement in P300-based brain-computer interface. PLoS ONE 2013;8:e60608.
• [21] Schröder M, Lal TN, Hinterberger T, Bogdan M, Hill NJ, Birbaumer N, et al. Robust EEG channel selection across subjects for brain-computer interfaces. EURASIP J Appl Signal Process 2005;2005:3103–12.
• [22] Colwell KA, Ryan DB, Throckmorton CS, Sellers EW, Collins LM. Channel selection methods for the P300 Speller. J Neurosci Methods 2014 Jul;232:6–15.
• [23] Speier W, Deshpande A, Pouratian N. A method for optimizing EEG electrode number and configuration for signal acquisition in P300 speller systems. Clin Neurophysiol 2015;126:1171–7.
• [24] Gao W, Guan J-a, Gao J, Zhou D. Multi-ganglion ANN based feature learning with application to P300-BCI signal classification. Biomed Signal Process Control 2015;18:127–37.
• [25] Jin J, Allison BZ, Brunner C, Wang B, Wang X, Zhang J, et al. P300 Chinese input system based on Bayesian LDA. Biomed Tech (Berl) 2010 Feb;55:5–18.
• [26] Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng 2004;51:1034–43.
• [27] Rakotomamonjy A, Guigue V. BCI competition III: dataset II-ensemble of SVMs for BCI P300 speller. IEEE Trans Biomed Eng 2008 Mar;55:1147–54.
• [28] Kee C-Y, Ponnambalam S, Loo C-K. Multi-objective genetic algorithm as channel selection method for P300 and motor imagery data set. Neurocomputing 2015;161:120–31.
• [29] Theodoridis S, Koutroumbas K. Pattern recognition. Fourth Edition. Academic Press; 2008.
• [30] Storn R, Price K. Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces vol. 3. ICSI Berkeley; 1995.
• [31] Das S, Suganthan PN. Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 2011;15:4–31.
• [32] Vesterstrom J, Thomsen R. A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. Evolutionary Computation, 2004. CEC2004. Congress on. 2004. pp. 1980–7.
• [33] Pampará G, Engelbrecht AP, Franken N. Binary differential evolution. Evolutionary Computation, 2006. CEC 2006. IEEE Congress on. 2006. pp. 1873–9.
• [34] Wang L, Fu X, Mao Y, Menhas MI, Fei M. A novel modified binary differential evolution algorithm and its applications. Neurocomputing 2012;98:55–75.
• [35] Chen Y, Xie W, Zou X. A binary differential evolution algorithm learning from explored solutions. Neurocomputing 2015;149:1038–47.
• [36] Rivet B, Cecotti H, Maby E, Mattout J. Impact of spatial filters during sensor selection in a visual P300 brain-computer interface. Brain Topogr 2012 Jan;25:55–63.
• [37] Demšar J. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 2006;7:1–30.
• [38] Nemenyi P. Distribution-free multiple comparisons. Biometrics 1962;263.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).