The Application Of Component Identification In Brain Imaging Signal Extraction

The signal of interest in the brain imaging data is mixed with various noise signals with different weights,and the intensity of the noise signal is greater than the intensity of the signal of interest.The low signal-to-noise ratio of the data makes it difficult to recover the signal of interest in the imaging data.In order to extract useful information in brain imaging data,this paper proposes the idea of component selection,and proposes different component selection algorithms according to the temporal and spatial characteristics of the data.In the optical imaging experiment,the stimulus applied to the subject can induce the signal of interest in the imaging of the optical function.Based on the changed intensity of response signal before and after stimulation,this paper discusses the application of component selection in spatial analysis.And the proposed method and the traditional method are applied to the simulated data and the real data respectively.The experimental results show that the imaging data reconstructed by the component selection algorithm is more accurate and contains more stimulus related signals.In the optical imaging experiment,the activated region occupies part of the cortical imaging region,and the signal-to-noise ratio of the activated region is higher than that of other regions.Based on the inhomogeneous distribution of signal-to-noise ratio in optical data,this paper discusses the application of signal selection algorithm in brain imaging data analysis.In this paper,the signal selection algorithm is used to interact with the simulation data and the real data.The simulation results show that the accuracy of the time source signal recovered by the method is improved.Due to the lack of accurate evaluation results in the real data,there are not much discussion about the experimental results.