Abstract
To understand how the brain functions work and assist the diagnosis of the brain diseases, functional magnetic resonance imaging (fMRI) has been widely used. This paper proposes an effective scheme to deal with the issue of identifying the functional signals and suppress the background noise by using independent component analysis (ICA) technique. The fastICA is discussed and applied to solve the problem of blind separation of the source functional signals. Finally, an example with the real fMRI data set was carried out in terms of the proposed method. The experimental result demonstrated the effectiveness and the robustness of the ICA and the related blind source separation approach.
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© 2004 Springer-Verlag Berlin Heidelberg
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Shen, M., Xu, W., Huang, J., Beadle, P. (2004). Analysis of Functional MRI Image Using Independent Component Analysis. In: Liew, KM., Shen, H., See, S., Cai, W., Fan, P., Horiguchi, S. (eds) Parallel and Distributed Computing: Applications and Technologies. PDCAT 2004. Lecture Notes in Computer Science, vol 3320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30501-9_29
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DOI: https://doi.org/10.1007/978-3-540-30501-9_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24013-6
Online ISBN: 978-3-540-30501-9
eBook Packages: Computer ScienceComputer Science (R0)