Abstract
Independent component analysis (ICA) is often used to spatially filter event-related potentials (ERPs). When an oddball paradigm is applied to elicit ERPs, difference wave (DW, responses of deviant stimuli minus those of standard ones) is often used to remove the common responses between the deviant and the standard. Thus, DW can be produced first, and then ICA is used to decompose the DW. Or, ICA is performed on responses of the deviant and standard stimuli separately, and then DW is applied on the filtered responses. In this study, we compared the two approaches to analyzing mismatch negativity (MMN). We found that DW introduced noise in the time and space domains, resulting in more difficulty to obtain the spatial properties of MMN by ICA on DW. Thus, we suggest using ICA to spatially filter event-related responses of each stimulus; and then DW is produced by the filtered responses.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Luck, S.J.: An Introduction to the Event-Related Potential Technique. The MIT Press, Cambridge (2005)
Talsma, D.: Auto-adaptive averaging: detecting artifacts in event-related potential data using a fully automated procedure. Psychophysiology 45(2), 216–228 (2008)
Cong, F., Leppanen, P.H., Astikainen, P., Hamalainen, J., Hietanen, J.K., Ristaniemi, T.: Dimension reduction: additional benefit of an optimal filter for independent component analysis to extract event-related potentials. J. Neurosci. Methods 201(1), 269–280 (2011)
Dien, J.: Applying principal components analysis to event-related potentials: a tutorial. Dev. Neuropsychol. 37(6), 497–517 (2012)
Kalyakin, I., Gonzalez, N., Karkkainen, T., Lyytinen, H.: Independent component analysis on the mismatch negativity in an uninterrupted sound paradigm. J. Neurosci. Methods 174(2), 301–312 (2008)
Cong, F., Lin, Q.H., Kuang, L.D., Gong, X.F., Astikainen, P., Ristaniemi, T.: Tensor decomposition of EEG signals–a brief review. J. Neurosci. Methods 248, 59–69 (2015)
Cong, F., Nandi, A.K., He, Z., Cichocki, A., Ristaniemi, T.: Fast and effective model order selection method to determine the number of sources in a linear transformation model. In: Proceeding of the 2012 European Signal Processing Conference (EUSIPCO-2012), pp. 1870–1874 (2012)
Hyvarinen, A.: Independent component analysis: recent advances. Proc. R. Soc. A Math. Phys. Eng. Sci. 371, 1–19 (2013)
Makeig, S., Westerfield, M., Jung, T.P., Covington, J., Townsend, J., Sejnowski, T.J.: Functionally independent components of the late positive event-related potential during visual spatial attention. J. Neurosci. 19(7), 2665–2680 (1999)
Cong, F., Kalyakin, I., Zheng, C., Ristaniemi, T.: Analysis on subtracting projection of extracted independent components from EEG recordings. Biomedizinische Technik/Biomed. Eng. 56(4), 223–234 (2011)
Himberg, J., Hyvarinen, A., Esposito, F.: Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage 22(3), 1214–1222 (2004)
Acknowledgments
Authors thank the support from National Natural Science Foundation of China (Grant Nos. 81471742 & 81461130018). Cong thanks Dr. Nicole Landi in Haskins Laboratories in Yale University for providing their ERP data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Yang, T., Cong, F., Chang, Z., Liu, Y., Ristainiemi, T., Li, H. (2016). Individual Independent Component Analysis on EEG: Event-Related Responses Vs. Difference Wave of Deviant and Standard Responses. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-40663-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40662-6
Online ISBN: 978-3-319-40663-3
eBook Packages: Computer ScienceComputer Science (R0)