Abstract
Scalp EEG has been used as a clinical tool for the diagnosis and treatment of brain diseases. A generalized ICA algorithm modified by a kernel-based density estimation procedure is studied to separate EEG signals from tumor patients into spatially independent source signals. The algorithm allows artifactual signals to be removed from the EEG and isolates brain related signals into single ICA components. Their back-projection onto the scalp sensors provides topographic relations useful for a meaningful interpretation by the experienced physician.
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References
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© 2000 Springer-Verlag London
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Habl, M., Bauer, C., Ziegaus, C., Lang, E.W., Schulmeyer, F. (2000). Analyzing Brain Tumor related EEG Signals with ICA Algorithms. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_18
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DOI: https://doi.org/10.1007/978-1-4471-0513-8_18
Publisher Name: Springer, London
Print ISBN: 978-1-85233-289-1
Online ISBN: 978-1-4471-0513-8
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