Development of P300 detection by Combination of Time, Frequency and Spatial Feature Extraction Methods

Amini, Zahra; Abootalebi, Vahid; Sadeghi, Mohammad Taghi

doi:10.22041/ijbme.2011.13198

Development of P300 detection by Combination of Time, Frequency and Spatial Feature Extraction Methods

Document Type : Full Research Paper

Authors

Zahra Amini ¹

Vahid Abootalebi ²

Mohammad Taghi Sadeghi ³

¹ M.Sc Student, Electrical and Computer Engineering Department, Yazd University

² 2Assistant Professor, Electrical and Computer Engineering Department, Yazd University

³ Assistant Professor, Electrical and Computer Engineering Department, Yazd University

https://doi.org/10.22041/ijbme.2011.13198

Abstract

The aim of this paper is to design a pattern recognition based system to detect P300 component in multi-channel electroencephalogram (EEG) trials. This system has two main blocks, feature extraction and classification. In feature extraction block, in addition to conventional features namely morphological, frequency and wavelet features, some new features included intelligent segmentation, common spatial pattern (CSP) and combined features (CSP + Segmentation) have also been used. Three criteria were used for evaluation and selection of a feature set by choosing a subset of the original features that contains most of essential information. Firstly, a statistical analysis has been applied for evaluating the fitness of each feature in discriminating between target and non target signals. Secondly, each of these six groups of features was evaluated by a Linear Discriminant Analysis (LDA) classifier. Furthermore by using Stepwise Linear Discriminant Analysis (SWLDA), the best set of features was selected. Among these six feature vectors, intelligent segmentation was seen to be most efficient in classification of these signals. In classification phase, two linear classifiers -LDA and SWLDA- were used. The algorithm was described here has tested with dataset II from the BCI competition 2005. In this research, the best result for P300 detection is 97.05% .This result have proven to be more accurate than the results of previous works carried out in this filed.

Keywords

P300

Brain Computer Interface (BCI)

Pattern recognition

Feature Extraction

Classification

[1] Polich J., “P300 in Clinical Applications,” in Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, E. NiederMeyer and F. Lopes Da Silva, Eds., 4th ed. Baltimore, Maryland: Lippincott Williams and Wilkins, 2000, ch. 58: 1073-1091.

[2] Wolpaw J.R., Birbaumer N., McFarland D. J., Pfurtscheller G., Vaughan T. M., Brain–computer interfaces for communication and control; J. Clin. Neurophysiol, 2002; 113: 767–791.

[3] Bayliss J. D., “A Flexible Brain-Computer Interface,” Phd. Thesis, University of Rochester, Rochester, New York, 2001.

[4] Serby H., Yom-Tov E., Inbar G. F., An improved P300-based brain-computer interface; IEEE Trans. on Neural Systems and Rehabilitation Eng., 2005; 13(1): 89-98.

[5] BCI Competition 2005. ida.first.fraunhofer.de/projects/bci/competition_ii

[6] Rakotomamonjy A., Guigue V., BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller; IEEE Trans.on Biomed. Eng., 2008; 55(3): 1147-1154.

[7] Salvaris M., Sepulveda F., Wavelets and ensemble of FLDs for P300 classification; 4th Int. IEEE/EMBS Conf. on Neural Eng., 2009; 9: 339-342.

[8] Yang L., Zongtan Z., Dewen H., Guohua D., T-weighted Approach for Neural Information Processing in P300 based Brain-Computer Interface; Int. Conf. on Neural Net. and Brain, 2005; 5(3):.1535-1539.

[9] قشونی مجید، خلیل زاده محمدعلی، بهبود تشخیص مؤلفه های شناختی در سیگنال ERP تک ثبت با استفاده از فضای ویژگی جدید و الگوریتم ژنتیک، دوازدهمین کنفرانس مهندسی پزشکی ایران، آبان 1384.

[10] ابوطالبی وحید، تجزیه و تحلیل مؤلفه های شناختی سیگنال الکتریکی مغز و کاربرد آن در دروغ سنجی، پایان‌نامه دکتری مهندسی پزشکی- بیوالکتریک، دانشگاه صنعتی امیر کبیر، اردیبهشت 1385.

[11] سیدصالحی سیده زهره، ع. مطیع نصرآبادی علی، ابوطالبی وحید، تشخیص مؤلفه P300 با استفاده از مدلهای مخفی مارکوف، چهاردهمین کنفرانس مهندسی پزشکی ایران، بهمن 1386

[12] Seyyedsalehi Z., Nasrabadi A.M., Abootalebi V., Committee Machines and Quadratic B-spline Wavelet for the P300 Speller Paradigm; IEEE/ACS Conf. on Comp. Sys. And Appl. 2008: 866-869, 2008.

[13] Sakamoto Y., Aono M., Supervised Adaptive Downsampling for P300-based Brain Computer Interface; 31st Int. Conf. on IEEE EMBS, 2009: .567-570.

[14] Foley D. H., Sammon Jr. J. W., An Optimal Set of Discriminant Vectors; IEEE Trans. on Comp. 1975; 24(3): 281-289.

[15] Pavlidis T., Waveform Segmentation Through Functional Approximation; IEEE Trans. on Comp., 1973; 22(7): 689-697.

[16] Markazi S.A., Qazi S., Stergioulas L.S., Ramchurn A., Bunce D., Wavelet Filtering of the P300 Component in Event-Related Potentials; In Proc. 28th IEEE EMBS Conf., Med. and Biomed. Soc., 2006: 1719-1722.

[17] Bostanov V., BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram; IEEE Trans. on Biomed. Eng.2004;.51(6): 1057-1061.

[18] Pires G., Nunes U., Castelo-Branco M., P300 spatial filtering and coherence-based channel selection; 4th IEEE EMBS Conf. on Neural Eng., NER '09, 2009:.311-314.

[19] Jolliffe I. T., “Principal Component Analysis,” Second Edition. Springer Series in Statistics, 2002.

[20] Combaz1 A., Manyakov N.V., Chumerin1 N., Suykens J. A. K., Van Hulle M. M., Feature Extraction and Classification of EEG Signals for Rapid P300 Mind spelling; IEEE Int. Conf. on Machine Learning and Appl., 2009: 386-391.