Publications by authors named "Xueyu Jia"
Article Synopsis
- A diagnostic model for Bipolar Disorder (BD) during its depressive phase was developed using RNA data from patients, employing a combination of Random Forest and Feedforward Neural Networks for analysis.
- The study utilized multiple datasets to identify key differentially expressed genes and trained a neural network model while implementing techniques to avoid overfitting.
- The final model exhibited strong performance metrics, with a specificity of 0.769, sensitivity of 0.818, and an AUC of 0.832, concluding that this approach effectively classifies BD based on significant gene expression changes.
The original motor imagery electroencephalography (MI-EEG) data contains not only temporal features but also a large number of spatial features related to the distribution of electrodes on the brain. However, in the process of MI-EEG decoding, most of the current convolutional neural network (CNN) based methods do not make the utmost of the spatial features related to electrode distribution.In this study, we adopt a concise 3D representation for the MI-EEG data to take full advantage of the spatial features and propose a two-branch 3D CNN (TB-3D CNN) for the 3D representation of MI-EEG data.
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