Abstract
Single trial electroencephalogram classification is indispensable in online brain–computer interfaces (BCIs) A classification method called adaptive Kernel Fisher Support Vector Machine (KF-SVM) is designed and applied to single trial EEG classification in BCIs. The adaptive KF-SVM algorithm combines adaptive idea, SVM and within-class scatter inspired from kernel fisher. Firstly, the within-class scatter matrix of a feature vector is calculated. And to construct a new kernel, this scatter is incorporated into the kernel function of SVM. Ultimately, the recognition result is calculated by the SVM whose kernel has been changed. The proposed algorithm simultaneously maximizes the discrimination between classes and also considers the within-class dissimilarities, which avoids some disadvantages of traditional SVM. In addition, the within-class scatter matrix of adaptive KF-SVM is updated trial by trail, which enhances the online adaptation of BCIs. Based on the EEG data recorded from seven subjects, the new approach achieved higher classification accuracies than the standard SVM, KF-SVM and adaptive linear classifier. The proposed scheme achieves the average performance improvement of 5.8%,5.2% and 3.7% respectively compared to other three schemes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G., et al.: Brain–computer interface technology: a review of the first international meeting. IEEE Trans. Rehabil. Eng. 8, 164–173 (2000)
Arvaneh, M., Guan, C.T., Ang, K.K., Quek, C.: Optimizing spatial filters by minimizing within-class dissimilarities in electroencephalogram-based brain–computer Interface. IEEE Trans. Neural Netw. Learn. Syst. 24(4), 610–619 (2013)
Liu, G.Q., Zhang, D.G., Meng, J.J., Huang, G., Zhu, X.Y.: Unsupervised adaptation of electroencephalogram signal processing based on fuzzy C-means algorithm. Int. J. Adapt. Control Signal Process. 26, 482–495 (2012)
Wang, Y.-T., Nakanishi, M., Wang, Y., Wei, C.-S., Cheng, C.-K., Jung, T.-P.: An online brain-computer interface based on SSVEPs measured from non-hair-bearing areas. IEEE Trans. Neural Syst. Rehabil. Eng. 25(1), 11–18 (2017)
Mondini, V., Mangia, A.L., Cappello, A.: EEG-Based BCI system using adaptive features extraction and classification procedures. Comput. Intell. Neurosci. 2016, 1–14 (2016)
Yan, W., Ge, Y.B.: A novel method for motor imagery EEG adaptive classification based biomimetic pattern recognition. Neurocomputing 116, 280–290 (2013)
Seo, D., et al.: Wireless recording in the peripheral nervous system with ultrasonic neural dust neuron neuroresource wireless recording in the peripheral nervous system with ultrasonic neural dust. Neuron 91, 1–11 (2016)
Yang, H., Sakhavi, S., Kai, K.A., Guan, C.: On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification. In: 2015 IEEE 37th Annual International Conference on Engineering in Medicine and Biology Society (EMBC), pp. 2620–2623 (2015)
Hsu, W.Y.: EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier. Comput. Biol. Med. 41, 633–639 (2011)
Wu, D.: Online and Offline Domain Adaptation for Reducing BCI Calibration Effort. IEEE Transactions on human-machine Systems, pp. 1–14 (2016)
Ye, Q.L., Zhao, C.X., Ye, N.: A New SVM classification approach via minimum within-class variance. J. Comput. Inf. Syst. 6(1), 39–45 (2011)
Spüler, M., Rosenstiel, W., Bogdan, Martin: Adaptive SVM-based classification increases performance of a MEG-based brain-computer interface (BCI). In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012. LNCS, vol. 7552, pp. 669–676. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33269-2_84
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. 51, 1073–1076 (2004)
Hema Rajini, N., Bhavani, R.: Automatic classification of computed tomography brain images using ANN, k-NN and SVM. AI Soc. 29, 97–102 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, B., Fan, C., Jia, J., Chen, S., Wang, J. (2017). Adaptive KF-SVM Classification for Single Trial EEG in BCI. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_4
Download citation
DOI: https://doi.org/10.1007/978-981-10-6370-1_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6369-5
Online ISBN: 978-981-10-6370-1
eBook Packages: Computer ScienceComputer Science (R0)