A Criterion to Evaluate Feature Vectors Based on ANOVA Statistical Analysis
| Autor: | Abbas Salami, Mohammad Hasan Moradi, Farnaz Ghassemi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Mahalanobis distance
Computer science business.industry Feature vector 0206 medical engineering Pattern recognition 02 engineering and technology Mutual information 020601 biomedical engineering F-distribution symbols.namesake 020204 information systems Scoring algorithm Linear regression 0202 electrical engineering electronic engineering information engineering symbols Artificial intelligence Greedy algorithm business Eigendecomposition of a matrix |
| Zdroj: | 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME). |
| DOI: | 10.1109/icbme.2017.8430266 |
| Popis: | The objective of this research is to evaluate feature vectors based on statistical analysis, focusing on application in brain-computer interface (BCI) domain. Common spatial pattern (CSP) is one of the most frequently used algorithms in BCI to extract features from electroencephalogram (EEG). However, since CSP would be a greedy algorithm by solving it through eigenvalue decomposition method, choosing features in a sequential way does not necessarily result in the minimal achievable classification error for higher than 2-dimensional feature vectors. To overcome this issue, Unbalanced Factorial ANOVA (UF-ANOVA) analysis based on linear regression has been used in order to evaluate features extracted from CSP algorithm. Finally, a criterion based on Mahalanobis distance and F distribution parameter resulted from ANOVA table is introduced to evaluate feature vectors. It is shown that proposed criterion is compatible with widely used criterions such as Fisher score (FS) and Mutual information (MI). Moreover, proposed analysis is not limited to one-dimensional feature vectors and can be applied to higher dimensions. |
| Databáze: | OpenAIRE |
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