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Title: Compression of EEG using tensor decomposition
Authors: Paramanathan Lakshmikanthan.
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2011
Abstract: Modem applications of EEG require acquisition, storage and transmission of large amount of EEG data. Therefore efficient data compression is a must in order to avoid the complexities in handling the EEG data recorded from multiple channels. The Tensor de- compositions have been widely used in the analysis of multidimensional data. During the last decade, the usage of tensors was extended to diverse applications including image and signal processing, feature extraction and pattern recognition of brain waves. The success- full application of tensor methods for Brain wave analysis and the natural representation of EEG provided by tensors suggested that it can be effectively used for EEG compression as well.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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