1 citations · 1 across the 2 of their papers we have counts for
4 papers
TransformEEG: Towards Improving Model Generalizability in Deep Learning-based EEG Parkinson's Disease Detection
Federico Del Pup, Riccardo Brun, Filippo Iotti +7
Electroencephalography (EEG) is establishing itself as an important, low-cost, noninvasive diagnostic tool for the early detection of Parkinson's Disease (PD). In this context, EEG…
The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: a preliminary study
Federico Del Pup, Andrea Zanola, Louis Fabrice Tshimanga +3
Deep learning is significantly advancing the analysis of electroencephalography (EEG) data by effectively discovering highly nonlinear patterns within the signals. Data partitionin…
xEEGNet: Towards Explainable AI in EEG Dementia Classification
Andrea Zanola, Louis Fabrice Tshimanga, Federico Del Pup +2
This work presents xEEGNet, a novel, compact, and explainable neural network for EEG data analysis. It is fully interpretable and reduces overfitting through major parameter reduct…
The more, the better? Evaluating the role of EEG preprocessing for deep learning applications
Federico Del Pup, Andrea Zanola, Louis Fabrice Tshimanga +2
The last decade has witnessed a notable surge in deep learning applications for the analysis of electroencephalography (EEG) data, thanks to its demonstrated superiority over conve…