Study of discriminant analysis applied to motor imagery bipolar data
We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear a...
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| Published in | Medical & biological engineering & computing Vol. 45; no. 1; pp. 61 - 68 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Springer Nature B.V
01.01.2007
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0140-0118 1741-0444 |
| DOI | 10.1007/s11517-006-0122-5 |
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| Abstract | We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain-computer interface research. Differences in classification accuracy of the classifiers were found when using different amounts of data; if a small amount was available, the best classifier was linear discriminant analysis (LDA) and if enough data were available all three classifiers performed very similar. This suggests that the effort needed to find regularizing parameters for RDA can be avoided by using LDA. |
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| AbstractList | We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain-computer interface research. Differences in classification accuracy of the classifiers were found when using different amounts of data; if a small amount was available, the best classifier was linear discriminant analysis (LDA) and if enough data were available all three classifiers performed very similar. This suggests that the effort needed to find regularizing parameters for RDA can be avoided by using LDA.We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain-computer interface research. Differences in classification accuracy of the classifiers were found when using different amounts of data; if a small amount was available, the best classifier was linear discriminant analysis (LDA) and if enough data were available all three classifiers performed very similar. This suggests that the effort needed to find regularizing parameters for RDA can be avoided by using LDA. We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain-computer interface research. Differences in classification accuracy of the classifiers were found when using different amounts of data; if a small amount was available, the best classifier was linear discriminant analysis (LDA) and if enough data were available all three classifiers performed very similar. This suggests that the effort needed to find regularizing parameters for RDA can be avoided by using LDA. We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain-computer interface research. Differences in classification accuracy of the classifiers were found when using different amounts of data; if a small amount was available, the best classifier was linear discriminant analysis (LDA) and if enough data were available all three classifiers performed very similar. This suggests that the effort needed to find regularizing parameters for RDA can be avoided by using LDA.[PUBLICATION ABSTRACT] |
| Author | Scherer, Reinhold Vidaurre, Carmen Pfurtscheller, Gert Schlögl, Alois Cabeza, Rafael |
| Author_xml | – sequence: 1 givenname: Carmen surname: Vidaurre fullname: Vidaurre, Carmen – sequence: 2 givenname: Reinhold surname: Scherer fullname: Scherer, Reinhold – sequence: 3 givenname: Rafael surname: Cabeza fullname: Cabeza, Rafael – sequence: 4 givenname: Alois surname: Schlögl fullname: Schlögl, Alois – sequence: 5 givenname: Gert surname: Pfurtscheller fullname: Pfurtscheller, Gert |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/17139517$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Brain - physiology Discriminant Analysis Humans Imagination Movement Studies User-Computer Interface |
| Title | Study of discriminant analysis applied to motor imagery bipolar data |
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