An important problem that hinders the use of supervised classification algorithms for brain imaging is that the number of variables per single subject far exceeds the number of training subjects available. Deriving multivariate measures of variable i...
Tracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the...
Machine learning technique has long been utilized to assist disease diagnosis, increasing clinical physicians' confidence in their decision and expediting the process of diagnosis. In this case, machine learning technique serves as a tool for disting...
Pattern recognition algorithms decode emotional brain states by using functional connectivity measures which are extracted from EEG signals as input to the statistical classifiers. An open-access EEG dataset for emotional state analysis is used to cl...
Despite enormous progress in machine learning, artificial neural networks still lag behind brains in their ability to generalize to new situations. Given identical training data, differences in generalization are caused by many defining features of a...
Machine learning is a promising approach for electroencephalographic (EEG) trials classification. Its efficiency is largely determined by the feature extraction and selection techniques reducing the dimensionality of input data. Dimensionality reduct...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
In this paper, we proposed and validated a novel and fully automatic pipeline for simultaneous tissue classification and lateral ventricle segmentation via a 2D U-net. The 2D U-net was driven by a 3D fully convolutional neural network (FCN). Multiple...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
Knowledge of the level of mental workload induced by any task is essential for optimizing load share among the operators. This helps in assessing their capability; besides, helping in task allocation. Since a persistently high workload experienced by...
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