A Gaussian mixture model framework for feature selection of EEGs for narcolepsy


A Gaussian mixture model framework for feature selection of EEGs for narcolepsy – Although there are existing state-of-the-art models for EEG prediction, the quality of predictions remains very poor. We propose a method for combining the output of two separate time-domain time-dependent sub-sampling methods with a global filter method to generate a global model of EEG signals. Our objective is to use a global model learned by the two methods to form the local model of EEG signal while preserving its local structure. The learned global model is constructed by learning the time-frequency correspondence of EEG signal in a non-overlapping network. We validate our method on standard clinical EEG datasets consisting of 5-8 individuals. Our method is significantly faster to train on a single EEG dataset compared to the state-of-the-art methods when trained on it over a set of multiple individuals, thus outperforming a standard EEG predictor. We also show that the proposed method can significantly outperform the state-of-the-art method.

In this paper, we will show a new method to estimate the density of an ellipsoid which is composed of a two-dimensional Euclidean space. This method can be used for solving complex combinatorial optimization problems where the goal is to obtain the most likely solution. Our method uses a linear combination of the sum of all the solutions in this problem and uses a fast gradient descent method for solving the problem which only takes the solutions of the sum of the solutions in the Euclidean space. The problem is efficiently solvable by the simple gradient descent method while keeping the Euclidean coordinates of the problem invariant, with no reference of the problem’s solution. We give a detailed description of the proposed method and we compare it with several commonly used solvers.

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A Gaussian mixture model framework for feature selection of EEGs for narcolepsy

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  • Convolutional neural network with spatiotemporal-convex relaxations

    Online Learning for High Dimensional Subspace AnalysisIn this paper, we will show a new method to estimate the density of an ellipsoid which is composed of a two-dimensional Euclidean space. This method can be used for solving complex combinatorial optimization problems where the goal is to obtain the most likely solution. Our method uses a linear combination of the sum of all the solutions in this problem and uses a fast gradient descent method for solving the problem which only takes the solutions of the sum of the solutions in the Euclidean space. The problem is efficiently solvable by the simple gradient descent method while keeping the Euclidean coordinates of the problem invariant, with no reference of the problem’s solution. We give a detailed description of the proposed method and we compare it with several commonly used solvers.


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