Abstract
Brain computer interface (BCI) is a communication pathway between brain and peripheral devices, which is promising in the field of rehabilitation and helps to improve the life quality of physically challenged people. Analysis of EEG signal is essential in non-invasive BCI system. However, because of EEG signal’s low signal-to-noise ratio and huge amount of data, signal analysis function in current BCI systems is rarely available online, which is inconvenient for system adaptation and calibration, as well as comprehension of data’s characteristics. To address the problem, this paper presents two features that are suitable for online visualization. Rhythm power indicates active brain region, and filtered ERSP (Event related spectrum power) is a substitute for original ERSP which provides information in signal’s frequency domain. Moreover, visualization of CSP (Common Spatial Pattern) feature is also realized which serves as an indicator of epochs’ quality.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Senhadji, L., Dillenseger, J.L., Wendling, F., Rocha, C., Kinie, A.: Wavelet Analysis of EEG for Three-Dimensional Mapping of Epileptic Events. Ann. Biomed. Eng. 23(5), 543–552 (1995)
Zhang, Z., Kawabatat, H., Liu, Z.Q.: EEG Analysis using Fast Wavelet Transform. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 4, pp. 2959–2964 (2000)
Wentrup, M.G., Buss, M.: Multi-class Common Spatial Patterns and Information Theoretic Feature Extraction. IEEE Trans. Biomed. Eng. 55(8), 1991–2000 (2008)
Ramoser, H., Muller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng. 20(5), 100–120 (1998)
Miwakeichi, F., Martinez-Montes, E., Valdes-Sosa, P.A., Nishiyama, N., Mizuhara, H., Yamaguchia, Y.: Decomposing EEG data into space-time-frequency components using Parallel Factor Analysis. NeuroImage 22(3), 1035–1045 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hong, K., Zhang, L., Li, J., Li, J. (2010). Multi-modal EEG Online Visualization and Neuro-Feedback. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-13318-3_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13317-6
Online ISBN: 978-3-642-13318-3
eBook Packages: Computer ScienceComputer Science (R0)