Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a technique for improving the feature extraction and classification stages in EEG-based Brain-Computer Interface (BCI) Systems. The problem can be formulated as Linear Matrix Inequalities (LMIs) and, therefore, be solved through robust computational tools. The idea is to represent the EEG signals using a sinusoidal signal basis in a given frequency range, and introducing a transformation to the signal, in this new representation,  parameterize the correlation matrix of the transformed signal. The parameterization is carried out in terms of an auxiliary matrix that is related to the transformation. The interest of these ideas are threefold. Firstly, it allows for a compact representation of the signals where the matrices representing the signals have considerably smaller dimensions. The second nice feature is that information about the frequency spectrum of the signals appear explicitly in the correlation matrix, which can help the feature extraction based on correlation matrices. Finally, the parameters of the transformation appear linearly in the correlation matrix and standard LMI techniques can be used to determine the best transformation for the classification problem, depending on how the problem is formulated. The classification is accomplished by taking advantage of Riemannian metrics that offers a more convenient notion of distance in the space of correlation matrices. A numerical example based on a motor imagery dataset is used to illustrate the proposed methodology.
['Cleison Silva', 'Rafael Duarte', 'Renan Goulart', 'Alexandre Trofino']
I am writing a research paper on a idea Towards a LMI approach to feature extraction improvements and classification by Riemann distance
[{"from": "human", "value": "I am writing a research paper on a idea Towards a LMI approach to feature extraction improvements and classification by Riemann distance"}, {"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 technique for improving the feature extraction and classification stages in EEG-based Brain-Computer Interface (BCI) Systems. The problem can be formulated as Linear Matrix Inequalities (LMIs) and, therefore, be solved through robust computational tools. The idea is to represent the EEG signals using a sinusoidal signal basis in a given frequency range, and introducing a transformation to the signal, in this new representation,  parameterize the correlation matrix of the transformed signal. The parameterization is carried out in terms of an auxiliary matrix that is related to the transformation. The interest of these ideas are threefold. Firstly, it allows for a compact representation of the signals where the matrices representing the signals have considerably smaller dimensions. The second nice feature is that information about the frequency spectrum of the signals appear explicitly in the correlation matrix, which can help the feature extraction based on correlation matrices. Finally, the parameters of the transformation appear linearly in the correlation matrix and standard LMI techniques can be used to determine the best transformation for the classification problem, depending on how the problem is formulated. The classification is accomplished by taking advantage of Riemannian metrics that offers a more convenient notion of distance in the space of correlation matrices. A numerical example based on a motor imagery dataset is used to illustrate the proposed methodology."}]