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Tytuł artykułu

Spatial and spatio-temporal filtering based on common spatial patterns and Max-SNR for detection of P300 component

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Recent advances in brain-computer interfaces (BCIs) have developed a new arena for designing systems to help disabled persons to communicate with the surrounding environment. P300 speller is one of the most famous BCI systems choosing the characters from a virtual keyboard through the analysis of EEG signals. P300 detection is an important processing step of these systems. The accuracy of P300 detection highly depends on the feature extraction method. In this study, the maximum signal to noise ratio (Max-SNR) has been used for feature extraction, which rarely applied in this area. This study presents a novel feature extraction technique, named spatio-temporal Max-SNR (ST.Max-SNR). Unlike the standard Max-SNR which only uses spatial patterns of a signal, the proposed method, separately consider the spatial and temporal patterns of the signal to enhance the accuracy of feature extraction. Due to the similarity of the common spatial pattern (CSP) and the Max-SNR algorithms, the performance of this technique and its extension, common Spatio- temporal pattern (CSTP), has been compared with the proposed method. Then, the LDA and SWLDA classifiers are used for classification of the features. Our experimental results show that the Max-SNR based spatio-temporal features lead to an average classification accuracy of 94.4 percent suggesting the best performance.
Twórcy
autor
  • Electrical Engineering Department Yazd University, Yazd, Iran
  • Electrical Engineering Department Yazd University, Yazd, Iran
  • Electrical Engineering Department Yazd University, Yazd, Iran
Bibliografia
  • [1] Shulman RG, Rothman DL, Hyder F. A BOLD search for baseline. Neuroimage 2007;36(2):277–81.
  • [2] Turnip A, Hong KS, Jeong MY. Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis. Biomed Eng Online 2011;10(1):1.
  • [3] Gao D, Li M, Li J, Liu Z, Yao D, Li G, et al. Effects of various typical electrodes and electrode gels combinations on MRI signal-to-noise ratio and safety issues in EEG-fMRI recording. Biocybernet Biomed Eng 2016;36(1):9–18.
  • [4] Wolpaw JR, McFarland DJ, Vaughan TM. Brain-computer interface research at the Wadsworth center. IEEE Trans Rehab Eng 2000;8:222–6.
  • [5] McFarland D, Wolpaw J. Brain-computer interfaces for communication and control. Commun ACM 2011;54:60–6.
  • [6] Abootalebi V, Moradi MH, Khalilzadeh MA. A new approach for EEG feature extraction in P300-based lie detection. Comput Methods Progr Biomed 2009;94:48–57.
  • [7] Farwell L.A. Method and apparatus for truth detection. U.S. Patent, 1995;(5): 406-956.
  • [8] NiederMeyer E, Lopes DaSilva F, editors. Electroencephalography: basic principles, clinical applications, and related fields. Baltimore, Maryland: Lippincott Williams and Wilkins; 2000.
  • [9] Thulasidas M, Guan C, Wu J. Robust classification of EEG signal for brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 2006;14(1):24–9.
  • [10] Amini Z, Abootalebi V, Sadeghi MT. Comparison of performance of different feature extraction methods in detection of P300. Biocybernet Biomed Eng 2013;33(1):3–20.
  • [11] Turnip A, Hong K-S. Classifying mental activities from EEG-P300 signals using adaptive neural network. Int J Innovative Comput Inform Control 2012;8:6429–43.
  • [12] Gao JF, Tian HJ, Yang Y, Yu XL, Li CH, Rao NN. A novel algorithm to enhance P300 in single trials: application to lie detection using F-score and SVM. PLoS One 2014;9:e109700.
  • [13] Koles ZJ. The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. Electroenc Clin Neurophys 1991;79(6):440–7.
  • [14] Pires G, Nunes U, Castelo-Branco M. Statistical spatial filtering for a P300-based BCI: Tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis. J Neurosci Methods 2011;195(2):270–81.
  • [15] Lenhardt A, Kaper M, Ritter H. An adaptive P300-based online brain computer interface. IEEE Trans Neural Syst and Rehabil Eng 2008;16(2):121–30.
  • [16] MuÄller KR, Vigario R, Meinecke F, Ziehe A. Blind source separation techniques for decomposing event related brain signals. Int J Bifurcation Chaos 2004;14(2):773–91.
  • [17] Ramoser H, Muller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehab Eng 2000;8(4):441–6.
  • [18] Lemm S, Blankertz B, Curio G, MÃller K-R. Spatio-spectral filters for improving classification of single trial EEG. IEEE Trans Biomed Eng 2005;52(9):1541–8.
  • [19] Dornhege G, Blankertz B, Krauledat M, Losch F, Curio G, Muller K-R. Combined optimization of spatial and temporal filters for improving brain-computer interfacing. IEEE Trans Biomed Eng 2006;53(11):2274–81.
  • [20] Yu K, Shen K, Shao S, Ng WC, Kwok K, Li X. Common spatio-temporal pattern for single-trial detection of event-related potential in rapid serial visual presentation triage. IEEE Trans Biomed Eng 2011;58(9):2513–20.
  • [21] Blankertz B. BCI Competition III Webpage; 2005 [Online]. Available: http//ida.first.fraunhofer.de/projects/bci/ competition iii.
  • [22] Vařeka L, Mautner P. The event-related potential data processing using art 2 network. Proc. Biomedical Engineering and Informatics (BMEI). 2012. pp. 605–9.
  • [23] Monzingo R, Miller T, editors. Introduction to Adaptive Arrays. New York: John Wiley & Sons; 1980.
  • [24] Krusienski DJ, Sellers EW, ois Cabestaing F, Bayoudh S, McFarland DJ, Vaughan TM, et al. A comparison of classification techniques for the P300 speller. J Neural Eng 2006;3(4):299–305.
  • [25] Mirghasemi H, Fazel-Rezai R, Shamsollahi MB. Analysis of P300 Classifiers in Brain Computer Interface Speller. Proc. 31th IEEE EMBS Conf. on Med. and Biomed. 2006. pp. 6205–8.
  • [26] Nijboer F, Sellers EW, Mellinger J, Jordan MA, Matuz T, Furdea A, et al. A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. J Clin Neurophysiol 2008;119(8):1909–16.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-122e64c1-e4ca-443e-95bb-c4aacc8d7eb4
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