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Erschienen in:
2005 | OriginalPaper | Buchkapitel
A General fMRI Linear Convolution Model Based Dynamic Characteristic
verfasst von : Hong Yuan, Hong Li, Zhijie Zhang, Jiang Qiu
Erschienen in: Advances in Natural Computation
Verlag: Springer Berlin Heidelberg
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General linear model (GLM) is a most popularly method of functional magnetic imaging (fMRI) data analysis. The key of this model is how to constitute the design-matrix to model the interesting effects better and separate noises. In this paper, the new general linear convolution model is proposed by introducing dynamic characteristic function as hemodynamic response function for the processing of the fMRI data. The method is implemented by a new dynamic function convolving with stimulus pattern as design-matrix to detect brain active signal. The efficiency of the new method is confirmed by its application into the real-fMRI data. Finally, real- fMRI tests showed that the excited areas evoked by a visual stimuli are mainly in the region of the primary visual cortex.