Testing for nonlinearity in time series: the method of surrogate data
Conference proceedings on Interpretation of time series from nonlinear mechanical systems
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In this paper, we proposed a local region structured noise reduction method for cortical optical imaging (OI). In our method, block-designed task paradigm was employed. Canonical correlation analysis (CCA) technique was used to extract the underlying structured sources voxel by voxel. The response signals were detected among structured sources by surrogate test based on the reduced autoregression model (ST-RARM) technique. The power of structured noise was eliminated from original time series and then the data were reconstructed. Monte-Carlo simulation was applied to demonstrate the validity of our method. The results showed that our method was more efficient in activated voxel detection compared to the generally used methods PCA, DCT. Further, by using our method the phase knowledge of response signals was well preserved in the reconstructed data and hence a more accurate estimate was obtained. The final activity mapping was generated by utilizing the knowledge of both response amplitude and phase. The vein artifacts were efficiently reduced. Six sets of true OI data collected from the hind-paw (HP) area of rat's cortex were processed and improved activity mappings were obtained.