Correlation Assisted Strong Uncorrelating Transform Complex Common Spatial Patterns for Spatially Distant Channel Data
| Autor: | Cheolsoo Park, Heejun Lee, Seungmin Lee, Jiwoo You, Youngjoo Kim |
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| Rok vydání: | 2018 |
| Předmět: |
Article Subject
General Computer Science Wilcoxon signed-rank test Computer science General Mathematics 0206 medical engineering 02 engineering and technology Motor Activity lcsh:Computer applications to medicine. Medical informatics lcsh:RC321-571 Correlation Motor imagery 0202 electrical engineering electronic engineering information engineering Humans lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry business.industry General Neuroscience Channel data Brain Electroencephalography Signal Processing Computer-Assisted Pattern recognition General Medicine Covariance 020601 biomedical engineering Random forest Imagination Spatial ecology lcsh:R858-859.7 020201 artificial intelligence & image processing Artificial intelligence business Algorithms Research Article |
| Zdroj: | Computational Intelligence and Neuroscience Computational Intelligence and Neuroscience, Vol 2018 (2018) |
| ISSN: | 1687-5273 1687-5265 |
| DOI: | 10.1155/2018/4281230 |
| Popis: | The Strong Uncorrelating Transform Complex Common Spatial Patterns (SUTCCSP) algorithm, designed for multichannel data analysis, has a limitation on keeping the correlation information among channels during the simultaneous diagonalization process of the covariance and pseudocovariance matrices. This paper focuses on the importance of preserving the correlation information among multichannel data and proposes the correlation assisted SUTCCSP (CASUT) algorithm to address this issue. The performance of the proposed algorithm was demonstrated by classifying the motor imagery electroencephalogram (EEG) dataset. The features were first extracted using CSP algorithms including the proposed method, and then the random forest classifier was utilized for the classification. Experiments using CASUT yielded an average classification accuracy of 78.10 (%), which significantly outperformed those of original CSP, Complex Common Spatial Patterns (CCSP), and SUTCCSP with p-values less than 0.01, tested by the Wilcoxon signed rank test. |
| Databáze: | OpenAIRE |
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