Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a new method for detecting activation in functional magnetic resonance imaging (fMRI) data.  project the fMRI time series on a low-dimensional subspace spanned by wavelet packets in order to create projections that are as non-Gaussian as possible. Our approach achieves two goals: it reduces the dimensionality of the problem by explicitly constructing a sparse approximation to the dataset and it also creates meaningful clusters allowing the separation of the activated regions from the clutter formed by the background time series.  use a mixture of Gaussian densities to model the distribution of the wavelet packet coefficients.  expect activated areas that are connected, and impose a spatial prior in the form of a Markov random field. Our approach was validated with in vivo data and realistic synthetic data, where it outperformed a linear model equipped with the knowledge of the true hemodynamic response.
['François G. Meyer', 'Xilin Shen']
I am writing a research paper on a idea Classification of fMRI Time Series in a Low-Dimensional Subspace With a Spatial Prior
[{"from": "human", "value": "I am writing a research paper on a idea Classification of fMRI Time Series in a Low-Dimensional Subspace With a Spatial Prior"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a new method for detecting activation in functional magnetic resonance imaging (fMRI) data.  project the fMRI time series on a low-dimensional subspace spanned by wavelet packets in order to create projections that are as non-Gaussian as possible. Our approach achieves two goals: it reduces the dimensionality of the problem by explicitly constructing a sparse approximation to the dataset and it also creates meaningful clusters allowing the separation of the activated regions from the clutter formed by the background time series.  use a mixture of Gaussian densities to model the distribution of the wavelet packet coefficients.  expect activated areas that are connected, and impose a spatial prior in the form of a Markov random field. Our approach was validated with in vivo data and realistic synthetic data, where it outperformed a linear model equipped with the knowledge of the true hemodynamic response."}]