Time-Frequency Data Reduction for Event Related Potentials: Combining Principal Component Analysis and Matching Pursuit
<p/> <p>Joint time-frequency representations offer a rich representation of event related potentials (ERPs) that cannot be obtained through individual time or frequency domain analysis. This representation, however, comes at the expense of increased data volume and the difficulty of inte...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2010-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://asp.eurasipjournals.com/content/2010/289571 |
| _version_ | 1818960319063523328 |
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| author | Bernat EdwardM Malone StephenM Iacono WilliamG Aviyente Selin |
| author_facet | Bernat EdwardM Malone StephenM Iacono WilliamG Aviyente Selin |
| author_sort | Bernat EdwardM |
| collection | DOAJ |
| description | <p/> <p>Joint time-frequency representations offer a rich representation of event related potentials (ERPs) that cannot be obtained through individual time or frequency domain analysis. This representation, however, comes at the expense of increased data volume and the difficulty of interpreting the resulting representations. Therefore, methods that can reduce the large amount of time-frequency data to experimentally relevant components are essential. In this paper, we present a method that reduces the large volume of ERP time-frequency data into a few significant time-frequency parameters. The proposed method is based on applying the widely used matching pursuit (MP) approach, with a Gabor dictionary, to principal components extracted from the time-frequency domain. The proposed PCA-Gabor decomposition is compared with other time-frequency data reduction methods such as the time-frequency PCA approach alone and standard matching pursuit methods using a Gabor dictionary for both simulated and biological data. The results show that the proposed PCA-Gabor approach performs better than either the PCA alone or the standard MP data reduction methods, by using the smallest amount of ERP data variance to produce the strongest statistical separation between experimental conditions.</p> |
| first_indexed | 2024-12-20T11:55:38Z |
| format | Article |
| id | doaj.art-ab75ddfe671c48a0a8a258deadb6aef0 |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-20T11:55:38Z |
| publishDate | 2010-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-ab75ddfe671c48a0a8a258deadb6aef02022-12-21T19:41:40ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-0120101289571Time-Frequency Data Reduction for Event Related Potentials: Combining Principal Component Analysis and Matching PursuitBernat EdwardMMalone StephenMIacono WilliamGAviyente Selin<p/> <p>Joint time-frequency representations offer a rich representation of event related potentials (ERPs) that cannot be obtained through individual time or frequency domain analysis. This representation, however, comes at the expense of increased data volume and the difficulty of interpreting the resulting representations. Therefore, methods that can reduce the large amount of time-frequency data to experimentally relevant components are essential. In this paper, we present a method that reduces the large volume of ERP time-frequency data into a few significant time-frequency parameters. The proposed method is based on applying the widely used matching pursuit (MP) approach, with a Gabor dictionary, to principal components extracted from the time-frequency domain. The proposed PCA-Gabor decomposition is compared with other time-frequency data reduction methods such as the time-frequency PCA approach alone and standard matching pursuit methods using a Gabor dictionary for both simulated and biological data. The results show that the proposed PCA-Gabor approach performs better than either the PCA alone or the standard MP data reduction methods, by using the smallest amount of ERP data variance to produce the strongest statistical separation between experimental conditions.</p>http://asp.eurasipjournals.com/content/2010/289571 |
| spellingShingle | Bernat EdwardM Malone StephenM Iacono WilliamG Aviyente Selin Time-Frequency Data Reduction for Event Related Potentials: Combining Principal Component Analysis and Matching Pursuit EURASIP Journal on Advances in Signal Processing |
| title | Time-Frequency Data Reduction for Event Related Potentials: Combining Principal Component Analysis and Matching Pursuit |
| title_full | Time-Frequency Data Reduction for Event Related Potentials: Combining Principal Component Analysis and Matching Pursuit |
| title_fullStr | Time-Frequency Data Reduction for Event Related Potentials: Combining Principal Component Analysis and Matching Pursuit |
| title_full_unstemmed | Time-Frequency Data Reduction for Event Related Potentials: Combining Principal Component Analysis and Matching Pursuit |
| title_short | Time-Frequency Data Reduction for Event Related Potentials: Combining Principal Component Analysis and Matching Pursuit |
| title_sort | time frequency data reduction for event related potentials combining principal component analysis and matching pursuit |
| url | http://asp.eurasipjournals.com/content/2010/289571 |
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