activity
20242026
collaborators

6 papers

cs.LG2026

Multimodal Surface EMG Hand Gesture Recognition Using Query-Based Transformers for Prosthetic Control

Federico Del Pup, Elisa Tentori, Manfredo Atzori

Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control. In this field, deep learning approaches have become the gold standard. However, c…

q-bio.GN2026

Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis

Mikele Milia, Louis Fabrice Tshimanga, Henning Mueller +2

Motivation: Transformer-based models are increasingly applied to large-scale single-cell transcriptomics, showing strong performance through self-supervised learning on millions of…

cs.CV2026

Token-UNet: A New Case for Transformers Integration in Efficient and Interpretable 3D UNets for Brain Imaging Segmentation

Louis Fabrice Tshimanga, Andrea Zanola, Federico Del Pup +1

We present Token-UNet, adopting the TokenLearner and TokenFuser modules to encase Transformers into UNets. While Transformers have enabled global interactions among input elements…

eess.SP2025

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…

cs.LG2025

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…

eess.SP2024

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…