SATEER: Subject-Aware Transformer for EEG-Based Emotion Recognition.

This study presents a Subject-Aware Transformer-based neural network designed for the Electroencephalogram (EEG) Emotion Recognition task (SATEER), which entails the analysis of EEG signals to classify and interpret human emotional states. SATEER processes the EEG waveforms by transforming them into Mel spectrograms, which can be seen as particular cases of images with the number of channels equal to the number of electrodes used during the recording process; this type of data can thus be processed using a Computer Vision pipeline. Distinct from preceding approaches, this model addresses the variability in individual responses to identical stimuli by incorporating a User Embedder module.

From EHR to Machine Learning: A Preliminary Report on an Ingestion Pipeline Based on JSON-LD.

In this paper, we present the preliminary experiments for the development of an ingestion mechanism to move data from Electronic Health Records to machine learning processes, based on the concept of Linked Data and the JSON-LD format.

Spatio-Temporal Image-Based Encoded Atlases for EEG Emotion Recognition.

Emotion recognition plays an essential role in human-human interaction since it is a key to understanding the emotional states and reactions of human beings when they are subject to events and engagements in everyday life. Moving towards human-computer interaction, the study of emotions becomes fundamental because it is at the basis of the design of advanced systems to support a broad spectrum of application areas, including forensic, rehabilitative, educational, and many others. An effective method for discriminating emotions is based on ElectroEncephaloGraphy (EEG) data analysis, which is used as input for classification systems.

Robust Federated Learning for Heterogeneous Model and Data.

Data privacy and security is an essential challenge in medical clinical settings, where individual hospital has its own sensitive patients data. Due to recent advances in decentralized machine learning in Federated Learning (FL), each hospital has its own private data and learning models to collaborate with other trusted participating hospitals. Heterogeneous data and models among different hospitals raise major challenges in robust FL, such as gradient leakage, where participants can exploit model weights to infer data.

MV-MS-FETE: Multi-view multi-scale feature extractor and transformer encoder for stenosis recognition in echocardiograms.

Background: aortic stenosis is a common heart valve disease that mainly affects older people in developed countries. Its early detection is crucial to prevent the irreversible disease progression and, eventually, death. A typical screening technique to detect stenosis uses echocardiograms; however, variations introduced by other tissues, camera movements, and uneven lighting can hamper the visual inspection, leading to misdiagnosis.