Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space
Objective. This review paper provides an integrated perspective of Explainable Artificial
Intelligence (XAI) techniques applied to Brain-Computer Interfaces (BCIs). BCIs use …
Intelligence (XAI) techniques applied to Brain-Computer Interfaces (BCIs). BCIs use …
Impact of nutritional factors in blood glucose prediction in type 1 diabetes through machine learning
G Annuzzi, A Apicella, P Arpaia, L Bozzetto… - IEEE …, 2023 - ieeexplore.ieee.org
Type 1 Diabetes (T1D) is an autoimmune disease that affects millions of people worldwide.
A critical issue in T1D patients is the managing of Postprandial Glucose Response (PGR) …
A critical issue in T1D patients is the managing of Postprandial Glucose Response (PGR) …
Relevance-based channel selection in motor imagery brain–computer interface
Objective. Channel selection in the electroencephalogram (EEG)-based brain–computer
interface (BCI) has been extensively studied for over two decades, with the goal being to …
interface (BCI) has been extensively studied for over two decades, with the goal being to …
Exploring nutritional influence on blood glucose forecasting for Type 1 diabetes using explainable AI
G Annuzzi, A Apicella, P Arpaia… - IEEE journal of …, 2023 - ieeexplore.ieee.org
Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar
control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates …
control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates …
Predicting and monitoring blood glucose through nutritional factors in type 1 diabetes by artificial neural networks
G Annuzzi, L Bozzetto, A Cataldo, S Criscuolo… - Acta IMEKO, 2023 - acta.imeko.org
The monitoring and management of Postprandial Glucose Response (PGR), by
administering an insulin bolus before meals, is a crucial issue in Type 1 Diabetes (T1D) …
administering an insulin bolus before meals, is a crucial issue in Type 1 Diabetes (T1D) …
XAI-ADS: An Explainable Artificial Intelligence Framework for Enhancing Anomaly Detection in Autonomous Driving Systems
The advent of autonomous driving systems has given rise to pressing cybersecurity issues
regarding the vulnerability of vehicular ad hoc networks (VANETs) to potential attacks. This …
regarding the vulnerability of vehicular ad hoc networks (VANETs) to potential attacks. This …
Strategies to exploit XAI to improve classification systems
Abstract Explainable Artificial Intelligence (XAI) aims to provide insights into the decision-
making process of AI models, allowing users to understand their results beyond their …
making process of AI models, allowing users to understand their results beyond their …
Entropy and Coherence Features in EEG-Based Classification for Alzheimer's Disease Detection
S Criscuolo, A Cataldo, E De Benedetto… - 2024 IEEE …, 2024 - ieeexplore.ieee.org
Alzheimer's disease (AD) is a progressive neurode-generative condition that impacts
cognitive functions and the overall quality of life of millions of individuals worldwide. Early …
cognitive functions and the overall quality of life of millions of individuals worldwide. Early …
Explainable data poison attacks on human emotion evaluation systems based on EEG signals
The major aim of this paper is to explain the data poisoning attacks using label-flipping
during the training stage of the electroencephalogram (EEG) signal-based human emotion …
during the training stage of the electroencephalogram (EEG) signal-based human emotion …
[PDF][PDF] SHAP-based Explanations to Improve Classification Systems.
Abstract Explainable Artificial Intelligence (XAI) is a field usually dedicated to offering
insights into the decisionmaking mechanisms of AI models. Its purpose is to enable users to …
insights into the decisionmaking mechanisms of AI models. Its purpose is to enable users to …