Consistency of model-derived predictive patterns as a function of sample diversity. by Oualid Benkarim (9707986)

Classification accuracy and information transfer rate (ITR) as a function of the measurement set-up and the number of combined trials. by Dovilė Kurmanavičiūtė (21072549)

Confusion matrices of machine learning models trained on data oversampled by SMOTE and data trained with class weights given to the loss function. by Lucas Teoh (12878206)

Spatial characterization and cell-type-specific genes in mouse brain tissue. by Huamei Li (8815955)

List of Abbreviations by Gursimran Singh (575288)

Subjects:

The results of ICA performed using PyNoetic. by Gursimran Singh (575288)

Subjects:

Data_Sheet_1_A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks.docx by Maria Grazia Puxeddu (10211162)

“…Finally, as a proof of concept, we show an application of the algorithms to real functional brain networks derived from EEG signals collected at rest with closed and open eyes. …”

Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

Subjects:

Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

The workflow used to analyze imbalanced and balanced sequences. by Solomon Shiferaw Beyene (9143178)

Performances of Naïve Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM) and K- Nearest Neighbors (KNN). by Solomon Shiferaw Beyene (9143178)

Accuracy, sensitivity, specificity and F-score. by Solomon Shiferaw Beyene (9143178)

The figures showed a comparison of the balanced and imbalanced sequences and performance of classifiers. by Solomon Shiferaw Beyene (9143178)

Out-of-distribution prediction and performance of predictive models as a function of sample diversity. by Oualid Benkarim (9707986)

Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

Subjects:

Penalty functions used in different methods. by Souvik Seal (12437166)

Application of Cell2Spatial to 10× Genomics Xenium mouse brain data. by Huamei Li (8815955)

Estimation accuracy across different structural and functional components of the BVEP model. by Meysam Hashemi (4114738)

“…<p>(<b>A</b>) Excitability values used for simulation () versus the estimated values () for a selected brain region (node number 6). As the node dynamics are varied by changing the excitability parameter (functional component), the model inversion by NUTS algorithm demonstrates an accurate and robust estimation by recovering the ground truth. …”

Image_1_Test-retest reliability of modular-relevant analysis in brain functional network.PDF by Xuyun Wen (414454)

“…Considering the repeatability of network partition depends heavily on network size and module detection algorithm, we constructed three types of brain functional networks for each subject by using a set of coarse-to-fine brain atlases and adopted four methods for single-subject module detection and twelve methods for group-level module detection.…”

Hierarchical decomposition of covariance in gene expression space brain is mirrored by a matching decomposition in brain space. by Stan Kerstjens (13410092)