Abstract
There are various kinds of methods on activated regions detection, including model-driven method and data-driven method, univariate method and multivariate method, frequency domain analysis and time-domain analysis etc. We investigated the problems of principal component analysis applied to activated regions detection,an autocovariance based principal component analysis method was proposed. Firstly,the time series were converted to the autocovariance series, and then the principal component analysis was employed. Meanwhile, the tactic of principal component selection was discussed. The validity of the proposed method was illustrated by experiments on a synthetic dataset and a real dataset. It was shown that the error rate of the new approach was lower compared with the principal component analysis itself.
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Liu, D., Tian, X., Zhu, L. (2014). Autocovariance Based PCA Method for fMRI Data. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_8
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DOI: https://doi.org/10.1007/978-3-319-09891-3_8
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