Document Type : Original Article
Authors
1 MS Student, Department of Biomedical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
2 Assistant Professor, Department of Biomedical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Abstract
Brain-Computer Interfaces (BCIs) are communication systems, which enable users to send commands to computers by using brain activity only; this activity being generally measured by Electroencephalography (EEG). BCIs are generally designed according to a pattern recognition approach, i.e., by extracting features from EEG signals, and by using a classifier to identify the user’s mental state from such features. In this study, we have considered the BCI Competition data sets 2b-2008; additionally, Multi-Taper Common Spatial Pattern (MTCSP) feature extraction method is used for extracting the features of right and left hand data, Logistic Regression (Logreg) classifier is chosen to classify the data sets. In this paper, TPR, FPR, ACC and k function are used as evaluation criteria. The comparison of the results with the results of the BCI competition 2008 has proved the effectiveness, high accuracy and resolution of the proposed method. The results have shown that MTCSP method provides even higher classification accuracy. It points out that utilizing suitable preprocessing to keep the EEG signal free of redundant information is for sure a very important in the BCI development.
Keywords
Main Subjects
Poland.
Burke, D.P., Kelly, S.P., Chazal, P., de Reilly, R.B., Finucane, C., 2005. A parametric feature extraction and
classification strategy for brain-computer interfacing. IEEE Trans. Neural Syst. Rehabil. Eng., 13, 12-17.
Erfanian, A., Erfani, A., 2004. EEG-based brain-computer interface for hand grasp control: feature extraction by
using ICA. Proceeding of 9th Annual Conference, International Functional Electrical Stimulation Society.
Guyton, A., 1956. Guyton and Hall Textbook of Medical Physiology.
Hinterberger, T., Schmidt, S., Neumann, N., Mellinger, J., Blankertz, B., Curio, G., Birbaumer, N., 2004. Braincomputer
communication and slow cortical potentials. IEEE Trans. Biomed. Eng., 51, 1011-1018.
Kolodziej, M., Majkowski, A., Rak, R.J., 2012. Linear discriminate analysis as EEG features reduction technique for
brain-computer interfaces. ISSV 0033-2097, R.88NR 3a.
Kwak, N., 2003. Feature extraction based on ICA for binary classification problems. IEEE Trans. Knowl. Data Eng.,
15(6), 1374-1388.
Leeb, R., Brunner, C., Muller-Putz, G.R., Pfurtscheller, G., 2008. BCI Competition 2008-Graz data set B.
Liu, B., Wang, M., Yu, L., Liu, Z., Yu, H., 2005. Study of feature classification methods in BCI based on neural
networks. In Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference
Shanghai, China, September 1-4.
Manoochehri, M., Moradi, M.H., Pfurtscheller, G., 2010. Are all features extraction methods subject-independent.
(ICBME2010), 3-4 November.
Mason, S.G., Birch, G.E., 2000. A brain-controlled switch for asynchronous control applications. IEEE Trans. Biomed.
Eng., 47, 1297-1307.
Mitchell, T.M., 2015. Generative and discriminative classifiers: Naive bayes and logistic regression. January 31.
Müller-Gerking, J., Pfurtscheller, G., Flyvbjerg, H., 1999. Designing optimal spatial filters for single-trial EEG
classification in a movement task. Clin. Neurophysiol., 110, 787-798.
Pfurtscheller, G., Neuper, C., 2001. Motor imagery and direct brain-computer communication. Proc IEEE, 89, 1123-
1134.
Pfurtscheller, G., Neuper, C., Schlögl, A., Lugger, K., 1998. Separability of EEG signals recorded during right and left
motor imagery using adaptive autoregressive parameters. IEEE Trans. Rehabil. Eng., 6, 316-325.
Ramoser, H., Muller-Gerking, J., Pfurtscheller, G., 2000. Optimal spatial filtering of single trial EEG during imagined
hand movement. IEEE Trans. Rehabil. Eng., 8(4), 441-446.
Serby, H., Yom-Tov, E., Inbar, G.F., 2005. An improved P300-based brain-computer interface. IEEE Trans. Neural
Syst. Rehabil. Eng., 13, 89-98.
Singh, A., 2001. Adaptive noise cancellation. Department of Electronics & Communication Netaji Subhas Institute
of Technology, February to May.
Suleiman, A.B., Toka, S., Fatehi, A.H., 2010. Features extraction techniques of EEG signal for BCI applications.
University of Mosul, Iraq.
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M., 2002. Brain-computer interfaces for
communication and control. Clin. Neurophysiol., 113(6), 767-791.
Wolpaw, J.R., McFarland, D.J., Vaughan, T.M., 2000. Brain computer interface research at the Wadsworth Center.
IEEE Trans. Rehabil. Eng., 8, 222-226.
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