Compression of EEG using tensor decomposition
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 an...
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| Format: | Thesis |
| Language: | English |
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2013
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| Online Access: | http://hdl.handle.net/10356/53168 |
| _version_ | 1811692194609758208 |
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| author | Paramanathan Lakshmikanthan. |
| author2 | Justin Dauwels |
| author_facet | Justin Dauwels Paramanathan Lakshmikanthan. |
| author_sort | Paramanathan Lakshmikanthan. |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:31:55Z |
| format | Thesis |
| id | ntu-10356/53168 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:31:55Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/531682023-07-04T16:04:02Z Compression of EEG using tensor decomposition Paramanathan Lakshmikanthan. Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering 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. Master of Science (Computer Control and Automation) 2013-05-30T04:49:15Z 2013-05-30T04:49:15Z 2011 2011 Thesis http://hdl.handle.net/10356/53168 en 64 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Paramanathan Lakshmikanthan. Compression of EEG using tensor decomposition |
| title | Compression of EEG using tensor decomposition |
| title_full | Compression of EEG using tensor decomposition |
| title_fullStr | Compression of EEG using tensor decomposition |
| title_full_unstemmed | Compression of EEG using tensor decomposition |
| title_short | Compression of EEG using tensor decomposition |
| title_sort | compression of eeg using tensor decomposition |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/53168 |
| work_keys_str_mv | AT paramanathanlakshmikanthan compressionofeegusingtensordecomposition |